html_url stringlengths 48 51 | title stringlengths 5 268 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 164 54.1k | embeddings list |
|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/1831 | Some question about raw dataset download info in the project . | Sure it would be nice to have an easier access to these paths !
The dataset builder could have a method to return those, what do you think ?
Feel free to work on this @albertvillanova , it would be a nice addition :)
Your suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `h... | Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic i... | 100 | Some question about raw dataset download info in the project .
Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class ... | [
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https://github.com/huggingface/datasets/issues/1831 | Some question about raw dataset download info in the project . | Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:
```python
builder_instance.dl_manager.downloaded_paths
``` | Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic i... | 19 | Some question about raw dataset download info in the project .
Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class ... | [
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https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | Hi @wumpusman
`datasets` has a caching mechanism that allows to cache the results of `.map` so that when you want to re-run it later it doesn't recompute it again.
So when you do `.map`, what actually happens is:
1. compute the hash used to identify your `map` for the cache
2. apply your function on every batch
... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 116 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
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https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | Hi @lhoestq ,
Thanks for the reply. It's entirely possible that is the issue. Since it's a side project I won't be looking at it till later this week, but, I'll verify it by disabling caching and hopefully I'll see the same runtime.
Appreciate the reference,
Michael | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 47 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
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-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | I believe this is an actual issue, tokenizing a ~4GB txt file went from an hour and a half to ~10 minutes when I switched from my pre-trained tokenizer(on the same dataset) to the default gpt2 tokenizer.
Both were loaded using:
```
AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
```
I trained the ... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 117 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
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-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | Hi @johncookds do you think this can come from one tokenizer being faster than the other one ? Can you try to compare their speed without using `datasets` just to make sure ? | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 33 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
-0.40404737,
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0.1453052163,
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0.241645202,
-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | Hi yes, I'm closing the loop here with some timings below. The issue seems to be at least somewhat/mainly with the tokenizer's themselves. Moreover legacy saves of the trainer tokenizer perform faster but differently than the new tokenizer.json saves(note nothing about the training process/adding of special tokens chan... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 124 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
-0.40404737,
-0.0130074006,
-0.1024704129,
0.1034294739,
0.1453052163,
-0.1225538552,
0.2550543845,
0.1890889108,
0.1306776851,
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-0.0542232022,
0.4988860786,
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-0.1320427358,
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0.2197590768,
0.0224167742,
0.241645202,
-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | @lhoestq ,
Hi, which version of datasets has datasets.set_caching_enabled(False)? I get
module 'datasets' has no attribute 'set_caching_enabled'. To hopefully get around this, I reran my code on a new set of data, and did so only once.
@johncookds , thanks for chiming in, it looks this might be an issue of Toke... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 182 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
-0.40404737,
-0.0130074006,
-0.1024704129,
0.1034294739,
0.1453052163,
-0.1225538552,
0.2550543845,
0.1890889108,
0.1306776851,
-0.006502152,
-0.0542232022,
0.4988860786,
-0.1724810451,
-0.2727595866,
-0.1320427358,
0.0650426075,
0.2197590768,
0.0224167742,
0.241645202,
-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | Thanks for the experiments @johncookds and @wumpusman !
> Hi, which version of datasets has datasets.set_caching_enabled(False)?
Currently you have to install `datasets` from source to have this feature, but this will be available in the next release in a few days.
> I'm trying to figure out why the overhead ... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 157 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
-0.40404737,
-0.0130074006,
-0.1024704129,
0.1034294739,
0.1453052163,
-0.1225538552,
0.2550543845,
0.1890889108,
0.1306776851,
-0.006502152,
-0.0542232022,
0.4988860786,
-0.1724810451,
-0.2727595866,
-0.1320427358,
0.0650426075,
0.2197590768,
0.0224167742,
0.241645202,
-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | @lhoestq,
I just checked that previous run time was actually 3000 chars. I increased it to 6k chars, again, roughly double.
SlowTokenizer **7.4 s** to **15.7 s**
Tokenizer: **276 ms** to **616 ms**
I'll post this issue on Tokenizer, seems it hasn't quite been raised (albeit I noticed a similar issue that mig... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 56 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
-0.40404737,
-0.0130074006,
-0.1024704129,
0.1034294739,
0.1453052163,
-0.1225538552,
0.2550543845,
0.1890889108,
0.1306776851,
-0.006502152,
-0.0542232022,
0.4988860786,
-0.1724810451,
-0.2727595866,
-0.1320427358,
0.0650426075,
0.2197590768,
0.0224167742,
0.241645202,
-0.0342... |
https://github.com/huggingface/datasets/issues/1830 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? | Hi,
I'm following up here as I found my exact issue. It was with saving and re-loading the tokenizer. When I trained then processed the data without saving and reloading it, it was 10x-100x faster than when I saved and re-loaded it.
Both resulted in the exact same tokenized datasets as well.
There is additionally ... | This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_u... | 93 | using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_to... | [
-0.40404737,
-0.0130074006,
-0.1024704129,
0.1034294739,
0.1453052163,
-0.1225538552,
0.2550543845,
0.1890889108,
0.1306776851,
-0.006502152,
-0.0542232022,
0.4988860786,
-0.1724810451,
-0.2727595866,
-0.1320427358,
0.0650426075,
0.2197590768,
0.0224167742,
0.241645202,
-0.0342... |
https://github.com/huggingface/datasets/issues/1827 | Regarding On-the-fly Data Loading | Hi @acul3
Issue #1776 talks about doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using this feature, though :)
I wanted to ask about on-the-fly data loading from the cache (before pre-processing). | Hi,
I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan | 48 | Regarding On-the-fly Data Loading
Hi,
I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan
Hi @acul3
Issue #1776 talks about doing on-the-fly data pre-processing, which I think is s... | [
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https://github.com/huggingface/datasets/issues/1827 | Regarding On-the-fly Data Loading | Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memory-mapped from an Arrow file on disk. Therefore there's almost no RAM usage even if your dataset contains TB of data.
Usually at training time only one batch of data at a time is loaded in memory.
Does that answer your qu... | Hi,
I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan | 66 | Regarding On-the-fly Data Loading
Hi,
I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan
Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memo... | [
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https://github.com/huggingface/datasets/issues/1825 | Datasets library not suitable for huge text datasets. | Hi ! Looks related to #861
You are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take... | Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this ... | 197 | Datasets library not suitable for huge text datasets.
Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with t... | [
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0.2025509626,
0.1469030827,
0.06441... |
https://github.com/huggingface/datasets/issues/1825 | Datasets library not suitable for huge text datasets. | How recently was `set_transform` added? I am actually trying to implement it and getting an error:
`AttributeError: 'Dataset' object has no attribute 'set_transform'
`
I'm on v.1.2.1.
EDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful. | Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this ... | 43 | Datasets library not suitable for huge text datasets.
Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with t... | [
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0.1773833036,
-0.0256084912,
0.1219858... |
https://github.com/huggingface/datasets/issues/1825 | Datasets library not suitable for huge text datasets. | Yes indeed it was added a few days ago. The code is available on master
We'll do a release next week :)
Feel free to install `datasets` from source to try it out though, I would love to have some feedbacks | Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this ... | 41 | Datasets library not suitable for huge text datasets.
Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with t... | [
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0.13408002... |
https://github.com/huggingface/datasets/issues/1825 | Datasets library not suitable for huge text datasets. | For information: it's now available in `datasets` 1.3.0.
The 2.0 is reserved for even cooler features ;) | Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this ... | 17 | Datasets library not suitable for huge text datasets.
Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with t... | [
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https://github.com/huggingface/datasets/issues/1825 | Datasets library not suitable for huge text datasets. | Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.
Feel free to update your Datasets version
```shell
pip install -U datasets
```
and see if it better suits your needs. | Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this ... | 35 | Datasets library not suitable for huge text datasets.
Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with t... | [
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https://github.com/huggingface/datasets/issues/1821 | Provide better exception message when one of many files results in an exception | Hi!
Thank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.
I could take on this issue.
On the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by p... | I find when I process many files, i.e.
```
train_files = glob.glob('rain*.csv')
validation_files = glob.glob(validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
```
I sometimes encounter an error due to one of the files being misformed (i.e. no dat... | 129 | Provide better exception message when one of many files results in an exception
I find when I process many files, i.e.
```
train_files = glob.glob('rain*.csv')
validation_files = glob.glob(validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
```
I... | [
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https://github.com/huggingface/datasets/issues/1818 | Loading local dataset raise requests.exceptions.ConnectTimeout | Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).
This should be fixed on master now. Feel free to install `datasets` from source to try it out.
The ... | Load local dataset:
```
dataset = load_dataset('json', data_files=["../../data/json.json"])
train = dataset["train"]
print(train.features)
train1 = train.map(lambda x: {"labels": 1})
print(train1[:2])
```
but it raised requests.exceptions.ConnectTimeout:
```
/Users/littlely/myvirtual/tf2/bin/python3.7 /Us... | 69 | Loading local dataset raise requests.exceptions.ConnectTimeout
Load local dataset:
```
dataset = load_dataset('json', data_files=["../../data/json.json"])
train = dataset["train"]
print(train.features)
train1 = train.map(lambda x: {"labels": 1})
print(train1[:2])
```
but it raised requests.exceptions.Connec... | [
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https://github.com/huggingface/datasets/issues/1817 | pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500 | Hi !
The error you have is due to the `input_ids` column not having the same number of examples as the other columns.
Indeed you're concatenating the `input_ids` at this line:
https://github.com/LuCeHe/GenericTools/blob/431835d8e13ec24dceb5ee4dc4ae58f0e873b091/KerasTools/lm_preprocessing.py#L134
However the oth... | I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end
https://github.com/LuCeHe/GenericTools/blob/maste... | 116 | pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500
I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the... | [
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https://github.com/huggingface/datasets/issues/1811 | Unable to add Multi-label Datasets | Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) | I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse... | 41 | Unable to add Multi-label Datasets
I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervi... | [
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https://github.com/huggingface/datasets/issues/1811 | Unable to add Multi-label Datasets | Thanks @yjernite @lhoestq
The template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed. | I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse... | 22 | Unable to add Multi-label Datasets
I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervi... | [
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https://github.com/huggingface/datasets/issues/1810 | Add Hateful Memes Dataset | Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images ... | ## Add Hateful Memes Dataset
- **Name:** Hateful Memes
- **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set)
- **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.04790.pdf)
- **Data:** [Thi... | 87 | Add Hateful Memes Dataset
## Add Hateful Memes Dataset
- **Name:** Hateful Memes
- **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set)
- **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.... | [
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https://github.com/huggingface/datasets/issues/1808 | writing Datasets in a human readable format | AFAIK, there is currently no built-in method on the `Dataset` object to do this.
However, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).
You can convert the Arrow table to a pandas dataframe to save t... | Hi
I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq | 101 | writing Datasets in a human readable format
Hi
I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq
AFAIK, there is currently no built-in method on the `Dataset` obj... | [
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https://github.com/huggingface/datasets/issues/1808 | writing Datasets in a human readable format | Indeed this works as long as you have enough memory.
It would be amazing to have export options like csv, json etc. !
It should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.
There is already an `export` method but currently the only export type that ... | Hi
I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq | 61 | writing Datasets in a human readable format
Hi
I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq
Indeed this works as long as you have enough memory.
It would be... | [
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https://github.com/huggingface/datasets/issues/1805 | can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index | Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.
But since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of ... | So, I have the following instances in my dataset
```
{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of
this increase in rotation?',
'answer': 'C',
'example_id': 'ARCCH_Mercury_7175875',
'options':[{'option_context': 'One effect of ... | 63 | can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index
So, I have the following instances in my dataset
```
{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of
this increase in rotation?',
'answer'... | [
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0.0515105985,
0.0849943459,
0.013422451,
0.0216208585,
-0.242585... |
https://github.com/huggingface/datasets/issues/1805 | can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index | I totally forgot to answer this issue, I'm so sorry.
I was able to get it working by installing `datasets` from source. Huge thanks! | So, I have the following instances in my dataset
```
{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of
this increase in rotation?',
'answer': 'C',
'example_id': 'ARCCH_Mercury_7175875',
'options':[{'option_context': 'One effect of ... | 24 | can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index
So, I have the following instances in my dataset
```
{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of
this increase in rotation?',
'answer'... | [
0.0690419376,
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0.5476049781,
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0.0515105985,
0.0849943459,
0.013422451,
0.0216208585,
-0.242585... |
https://github.com/huggingface/datasets/issues/1803 | Querying examples from big datasets is slower than small datasets | Hello, @lhoestq / @gaceladri : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet? | After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorp... | 30 | Querying examples from big datasets is slower than small datasets
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_datas... | [
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https://github.com/huggingface/datasets/issues/1803 | Querying examples from big datasets is slower than small datasets | Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.
I'll take a look at it soon and let you know | After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorp... | 39 | Querying examples from big datasets is slower than small datasets
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_datas... | [
-0.4054937661,
0.058644373,
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https://github.com/huggingface/datasets/issues/1803 | Querying examples from big datasets is slower than small datasets | My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.
I mean, when I split the training and then reloads the model and o... | After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorp... | 218 | Querying examples from big datasets is slower than small datasets
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_datas... | [
-0.4054937661,
0.058644373,
-0.0799715444,
0.131532535,
-0.0796942189,
-0.177691564,
0.2441748083,
0.4918093979,
-0.2313988209,
0.1801939607,
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0.1307258159,
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0.0807414576,
0.1248459145,
-0.0751321092,
-0.03452... |
https://github.com/huggingface/datasets/issues/1803 | Querying examples from big datasets is slower than small datasets | I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.
My guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.
However if we know in advance in which chunk the element is, and at what index it i... | After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorp... | 82 | Querying examples from big datasets is slower than small datasets
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_datas... | [
-0.4054937661,
0.058644373,
-0.0799715444,
0.131532535,
-0.0796942189,
-0.177691564,
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0.4918093979,
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0.1307258159,
-0.273406595,
0.0807414576,
0.1248459145,
-0.0751321092,
-0.03452... |
https://github.com/huggingface/datasets/issues/1803 | Querying examples from big datasets is slower than small datasets | I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.
... | After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorp... | 125 | Querying examples from big datasets is slower than small datasets
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_datas... | [
-0.4054937661,
0.058644373,
-0.0799715444,
0.131532535,
-0.0796942189,
-0.177691564,
0.2441748083,
0.4918093979,
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-0.273406595,
0.0807414576,
0.1248459145,
-0.0751321092,
-0.03452... |
https://github.com/huggingface/datasets/issues/1803 | Querying examples from big datasets is slower than small datasets | Hi ! Feel free to post a message on the [forum](https://discuss.huggingface.co/c/datasets/10). I'd be happy to help you with this.
In your post on the forum, feel free to add more details about your setup:
What are column names and types of your dataset ?
How was the dataset constructed ?
Is the dataset shuffled ... | After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_dataset("bookcorpus", split="train[:50%]")
b100 = load_dataset("bookcorp... | 95 | Querying examples from big datasets is slower than small datasets
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.
For example
```python
from datasets import load_dataset
b1 = load_dataset("bookcorpus", split="train[:1%]")
b50 = load_datas... | [
-0.4054937661,
0.058644373,
-0.0799715444,
0.131532535,
-0.0796942189,
-0.177691564,
0.2441748083,
0.4918093979,
-0.2313988209,
0.1801939607,
0.0814937055,
0.0550893359,
0.1250858158,
-0.1007566899,
0.1307258159,
-0.273406595,
0.0807414576,
0.1248459145,
-0.0751321092,
-0.03452... |
https://github.com/huggingface/datasets/issues/1797 | Connection error | Hi ! For future references let me add a link to our discussion here : https://github.com/huggingface/datasets/issues/759#issuecomment-770684693
Let me know if you manage to fix your proxy issue or if we can do something on our end to help you :) | Hi
I am hitting to the error, help me and thanks.
`train_data = datasets.load_dataset("xsum", split="train")`
`ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py` | 40 | Connection error
Hi
I am hitting to the error, help me and thanks.
`train_data = datasets.load_dataset("xsum", split="train")`
`ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py`
Hi ! For future references let me add a link to our discussion here... | [
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0.0... |
https://github.com/huggingface/datasets/issues/1796 | Filter on dataset too much slowww | When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.
```
ds_table = dataset.data.filter(mask=dataset['flag'])
``` | I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is taking too much time. I need to filter se... | 32 | Filter on dataset too much slowww
I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is takin... | [
-0.2712090909,
-0.0597310849,
-0.1147630438,
-0.2779482007,
-0.1706506312,
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0.2090245038,
0.2598770559,
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0.2453582436,
0.1307905763,
0.297056824,
0.196013242,
0.03... |
https://github.com/huggingface/datasets/issues/1796 | Filter on dataset too much slowww | Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.
Using a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.
Replacing the old table by the new one s... | I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is taking too much time. I need to filter se... | 162 | Filter on dataset too much slowww
I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is takin... | [
-0.2580145895,
-0.1224424988,
-0.0864248201,
-0.2601466179,
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0.3136885762,
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0.3481481373,
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0.2076894641,
0.0726487339,
0.2243814766,
0.3868041635,
0.0485... |
https://github.com/huggingface/datasets/issues/1796 | Filter on dataset too much slowww | Hi @lhoestq @ayubSubhaniya,
If there's no progress on this one, can I try working on it?
Thanks,
Gunjan | I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is taking too much time. I need to filter se... | 18 | Filter on dataset too much slowww
I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is takin... | [
-0.2252545655,
-0.2595493495,
-0.1091807112,
-0.2148157656,
-0.1230366528,
-0.269241482,
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0.2114839256,
0.2634299099,
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0.0279619396,
0.0908805579,
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0.1516524553,
0.1011282802,
0.2131099403,
0.4031715393,
0.039... |
https://github.com/huggingface/datasets/issues/1796 | Filter on dataset too much slowww | Sure @gchhablani feel free to start working on it, this would be very appreciated :)
This feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk | I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is taking too much time. I need to filter se... | 39 | Filter on dataset too much slowww
I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.
When I applied the `filter()` function it is takin... | [
-0.2547402978,
-0.2351186275,
-0.101451993,
-0.2257346809,
-0.142864421,
-0.287168771,
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0.2359779328,
0.3127667904,
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0.0621095262,
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0.1691475809,
0.0948601365,
0.2099194527,
0.3651646674,
0.0446... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | Hi !
Apache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.
Wikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 167 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
0.191167891,
0.0017937372,
-0.0041887765,
0.1869836748,
0.2713819742,
0.1396205425,
0.4213475585,
0.3950366378,
0.2294802219,
0.1781060398,
0.1076989919,
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0.0533492379,
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0.1017364934,
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0.312389791,
0.075500913,
-0.1031828523,
-0.18625827... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | Thanks for your reply!
I understood.
I tried again with installing apache-beam, add ` beam_runner="DirectRunner"` and an anther `mwparserfromhell` is also required so I installed it.
but, it also failed. It exited 1 without error message.
```py
import datasets
# BTW, 20200501.ja doesn't exist at wikipedia, s... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 279 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
0.0917947292,
0.0745990351,
0.0195859279,
0.2518456876,
0.257083565,
0.0409057848,
0.3200167716,
0.348071456,
0.1667262614,
0.1959525049,
0.2174152136,
0.0488915034,
-0.097461924,
-0.2695727646,
0.0972720534,
-0.3023028672,
0.2464137226,
0.0408719443,
-0.1644422412,
-0.23304018... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | Hi @miyamonz,
I tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.
Based on a similar issue [here](https://github.com/google-research/fixmatch/issues/23), it could be related to you... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 61 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
-0.0868909806,
0.1544121802,
0.034921281,
0.2511789203,
0.3533329964,
0.1083715335,
0.3212843537,
0.4499051571,
0.3734619021,
0.0088105854,
0.1023254767,
0.0063316659,
-0.0099076126,
-0.0225595571,
-0.0207930878,
-0.2547833025,
0.1721133143,
0.0012148418,
-0.2130485773,
-0.2989... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | thanks for your reply and sorry for my late response.
## environment
my local machine environment info
- Ubuntu on WSL2
`lsb_release -a`
```
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 20.04.2 LTS
Release: 20.04
Codename: focal
```
RTX 2070 super
Inside WS... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 606 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
-0.0164618678,
-0.1208428219,
-0.0449259691,
0.1289525926,
0.3602371216,
0.0117901508,
0.4635161459,
0.3990748525,
0.2657026649,
0.2212448716,
0.3692161143,
-0.0926491693,
0.0788545087,
-0.0503800772,
0.3171316981,
-0.3475855887,
0.389339447,
-0.102307491,
-0.0446153246,
-0.285... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .
Does the fix your proposed at #2037 helps in your case ?
And for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right whe... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 72 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
0.1117344648,
-0.0064414921,
0.0087710219,
0.3851287961,
0.376698494,
0.0904636085,
0.1866942942,
0.4343984425,
0.2380294055,
0.1273686588,
0.1705145091,
0.0224920511,
-0.0121388407,
-0.2175728083,
0.0293771736,
-0.3352377117,
0.2749055624,
-0.0193752274,
-0.1703933328,
-0.2335... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | the #2037 doesn't solve my problem directly, but I found the point!
https://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/wikipedia/wikipedia.py#L523
this `beam.transforms.Reshuffle()` cause the memory error.
it makes sense if I consider the shuffle means. Beam's reshuffl... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 111 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
0.1194349378,
0.110886246,
0.0094728423,
0.4295068383,
0.5010865927,
0.0540744588,
0.1819474697,
0.4008019567,
-0.0547775179,
0.3162616789,
0.111268647,
0.0270882044,
-0.1956252307,
-0.30971542,
0.08737766,
-0.3430970311,
0.2697753012,
-0.0960950926,
-0.248987332,
-0.2321131378... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | The reshuffle is needed when you use parallelism.
The objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the article... | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 73 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
0.0401643589,
0.0101207774,
-0.0058726529,
0.1293458045,
-0.0119505981,
0.0596709512,
0.3661441803,
0.3480099738,
0.022974778,
0.2797617912,
0.1604570746,
0.0736092255,
0.1980749965,
-0.1953661591,
-0.0845639408,
-0.2648459673,
0.3423891366,
-0.0130629241,
-0.0566422231,
-0.355... |
https://github.com/huggingface/datasets/issues/1790 | ModuleNotFoundError: No module named 'apache_beam', when specific languages. | Maybe the reshuffle step can be added only if the runner is not a DirectRunner ? | ```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to lo... | 16 | ModuleNotFoundError: No module named 'apache_beam', when specific languages.
```py
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets')
```
then `ModuleNotFoundError: No module named 'apache_beam'` happend.
The error doesn't appear when it's '20200501.en'.
I don't k... | [
-0.0378295816,
0.0517894439,
0.0459224246,
0.1618946493,
0.2217683345,
0.0495495535,
0.2877705991,
0.3032410443,
0.0259335935,
0.1799332201,
0.2910350263,
-0.0192489214,
0.1045148596,
-0.0232276116,
0.110032022,
-0.3740753531,
0.307103008,
-0.0370497182,
-0.0959917903,
-0.28183... |
https://github.com/huggingface/datasets/issues/1786 | How to use split dataset | By default, all 3 splits will be loaded if you run the following:
```python
from datasets import load_dataset
dataset = load_dataset("lambada")
print(dataset["train"])
print(dataset["valid"])
```
If you wanted to do load this manually, you could do this:
```python
from datasets import load_dataset
dat... | 
Hey,
I want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my pro... | 56 | How to use split dataset
Hey,
I want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing t... | [
-0.3074782789,
-0.046430856,
-0.0625263825,
0.4651073217,
0.1305699348,
0.203790769,
0.2765960097,
0.5807618499,
-0.4295652211,
0.0150460266,
-0.3925747275,
0.2540954351,
0.1838490069,
0.3340370059,
-0.1944947243,
-0.4850150943,
0.0906332433,
0.2447198927,
0.0513655134,
-0.1060... |
https://github.com/huggingface/datasets/issues/1785 | Not enough disk space (Needed: Unknown size) when caching on a cluster | Hi !
What do you mean by "disk_usage(".").free` can't compute on the cluster's shared disk" exactly ?
Does it return 0 ? | I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path")
OSError: Not eno... | 22 | Not enough disk space (Needed: Unknown size) when caching on a cluster
I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dat... | [
-0.077743046,
-0.4187169075,
-0.0846218616,
0.2431603819,
-0.0970101953,
0.1506602615,
0.0705344975,
0.340121299,
0.3962351978,
0.3933159411,
0.4183928967,
-0.1389312744,
-0.0912500247,
0.0038916988,
-0.1739585549,
0.2417322844,
-0.008229685,
-0.0606452934,
0.3455673754,
0.1274... |
https://github.com/huggingface/datasets/issues/1785 | Not enough disk space (Needed: Unknown size) when caching on a cluster | Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.
```python
>>> disk_usage(".")
usage(total=999999, used=999999, free=0)
``` | I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path")
OSError: Not eno... | 32 | Not enough disk space (Needed: Unknown size) when caching on a cluster
I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dat... | [
-0.077743046,
-0.4187169075,
-0.0846218616,
0.2431603819,
-0.0970101953,
0.1506602615,
0.0705344975,
0.340121299,
0.3962351978,
0.3933159411,
0.4183928967,
-0.1389312744,
-0.0912500247,
0.0038916988,
-0.1739585549,
0.2417322844,
-0.008229685,
-0.0606452934,
0.3455673754,
0.1274... |
https://github.com/huggingface/datasets/issues/1785 | Not enough disk space (Needed: Unknown size) when caching on a cluster | That's an interesting behavior...
Do you know any other way to get the free space that works in your case ?
Also if it's a permission issue could you try fix the permissions and let mus know if that helped ? | I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path")
OSError: Not eno... | 41 | Not enough disk space (Needed: Unknown size) when caching on a cluster
I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dat... | [
-0.077743046,
-0.4187169075,
-0.0846218616,
0.2431603819,
-0.0970101953,
0.1506602615,
0.0705344975,
0.340121299,
0.3962351978,
0.3933159411,
0.4183928967,
-0.1389312744,
-0.0912500247,
0.0038916988,
-0.1739585549,
0.2417322844,
-0.008229685,
-0.0606452934,
0.3455673754,
0.1274... |
https://github.com/huggingface/datasets/issues/1785 | Not enough disk space (Needed: Unknown size) when caching on a cluster | I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue | I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path")
OSError: Not eno... | 22 | Not enough disk space (Needed: Unknown size) when caching on a cluster
I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.
The exact error thrown:
```bash
>>> load_dat... | [
-0.077743046,
-0.4187169075,
-0.0846218616,
0.2431603819,
-0.0970101953,
0.1506602615,
0.0705344975,
0.340121299,
0.3962351978,
0.3933159411,
0.4183928967,
-0.1389312744,
-0.0912500247,
0.0038916988,
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0.2417322844,
-0.008229685,
-0.0606452934,
0.3455673754,
0.1274... |
https://github.com/huggingface/datasets/issues/1784 | JSONDecodeError on JSON with multiple lines | Hi !
The `json` dataset script does support this format. For example loading a dataset with this format works on my side:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
```
Can you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?
| Hello :),
I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
```
But, when I try loading a dataset with th... | 49 | JSONDecodeError on JSON with multiple lines
Hello :),
I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
`... | [
0.0756986365,
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0.3547828794,
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0.0634567812,
0.1587533802,
0.0187908541,
0.1641429663,
0.2806328535,
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0.2131018043,
0.0773413777,
-0.257919699,
0.0545292273,
0.2539742291,
0.2304694057,
0.0939232... |
https://github.com/huggingface/datasets/issues/1784 | JSONDecodeError on JSON with multiple lines | Hi Quentin!
I apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.
I repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked.
Closing this issue. Again, sorry for the bother.
Thanks... | Hello :),
I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
```
But, when I try loading a dataset with th... | 56 | JSONDecodeError on JSON with multiple lines
Hello :),
I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
`... | [
0.1118792519,
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0.5094956756,
0.366851002,
0.1452075243,
0.4116664827,
0.0697855949,
0.1164880544,
0.0775828138,
0.1227523834,
0.3139996231,
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0.1743166149,
0.1052535996,
-0.3321567178,
0.0563052371,
0.2422202677,
0.2812243104,
0.0448313... |
https://github.com/huggingface/datasets/issues/1783 | Dataset Examples Explorer | Hi @ChewKokWah,
We're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/ | In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.
Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a ... | 21 | Dataset Examples Explorer
In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.
Hope HuggingFace can re-enable the feature that at least allow viewing of ... | [
-0.3861372173,
-0.387398392,
-0.0618845783,
0.2974123359,
0.1567153335,
0.2989472449,
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0.355283916,
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0.3763423264,
0.0037851783,
0.2994420826,
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0.5288338661,
0.3926571906,
-0.2897696495,
0.0115510263,
0.2083533108,
-0.1008175164,
0.11488... |
https://github.com/huggingface/datasets/issues/1783 | Dataset Examples Explorer | Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise.
Hope similar feature can be retain in the future version. | In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.
Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a ... | 36 | Dataset Examples Explorer
In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.
Hope HuggingFace can re-enable the feature that at least allow viewing of ... | [
-0.3628718853,
-0.4238751531,
-0.0469129309,
0.3083789647,
0.1272352934,
0.2346737236,
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0.3707433939,
-0.0460053198,
0.3754512668,
0.0373595208,
0.2407731265,
-0.310693413,
0.5867728591,
0.4250448048,
-0.3711117506,
0.0176206063,
0.1814073175,
-0.0853758901,
0.08684... |
https://github.com/huggingface/datasets/issues/1781 | AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import | Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?
The PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?
```
pip install pyarrow --upgrade
``` | I'm using Colab. And suddenly this morning, there is this error. Have a look below!
| 44 | AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below!
| 46 | AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below!
.
Also now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).
I guess wha... | I'm using Colab. And suddenly this morning, there is this error. Have a look below!
| 72 | AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below!
| 22 | AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
I'm using Colab. And suddenly this morning, there is this error. Have a look below!
? | I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?
BTW, I tried raising `writer_batch_si... | 18 | [Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly ... | [
-0.3903960884,
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0.2288655192,
0.2126400024,
0.1509193927,
0.2128422856,
0.0664261207,
0.0356723927,
0.2065106... |
https://github.com/huggingface/datasets/issues/1776 | [Question & Bug Report] Can we preprocess a dataset on the fly? | It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py | I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?
BTW, I tried raising `writer_batch_si... | 21 | [Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly ... | [
-0.3749499619,
-0.3105452359,
-0.0253562815,
0.0781464577,
0.4190764725,
0.3276049495,
0.2523880303,
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-0.1120237559,
0.0602597371,
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0.1677816212,
0.318941921,
0.0842123181,
0.2063731849,
0.0334924795,
-0.00253722,
0.1758530... |
https://github.com/huggingface/datasets/issues/1776 | [Question & Bug Report] Can we preprocess a dataset on the fly? | Indeed I will submit a PR in a fez days to enable processing on-the-fly :)
This can be useful in language modeling for tokenization, padding etc.
| I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?
BTW, I tried raising `writer_batch_si... | 26 | [Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly ... | [
-0.380630374,
-0.196968928,
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-0.0068638455,
0.4007772505,
0.3399396241,
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0.2634272575,
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0.1849375218,
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0.2875475883,
0.2689658105,
0.1431528628,
0.1967976987,
0.0332214274,
0.0725331381,
0.237766... |
https://github.com/huggingface/datasets/issues/1776 | [Question & Bug Report] Can we preprocess a dataset on the fly? | Hi @acul3,
Please look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do. | I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache?
BTW, I tried raising `writer_batch_si... | 22 | [Question & Bug Report] Can we preprocess a dataset on the fly?
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly ... | [
-0.4329630733,
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0.0117293578,
0.4850178361,
0.2730878592,
0.3065050244,
0.2762750685,
-0.1747132987,
0.1026610509,
0.098205395,
0.1963545233,
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0.246045053,
0.2638713121,
0.1422840804,
0.2081042677,
0.0898797959,
-0.0966305137,
0.187596... |
https://github.com/huggingface/datasets/issues/1775 | Efficient ways to iterate the dataset | It seems that selecting a subset of colums directly from the dataset, i.e., dataset["column"], is slow. | For a large dataset that does not fits the memory, how can I select only a subset of features from each example?
If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this?
Thanks | 16 | Efficient ways to iterate the dataset
For a large dataset that does not fits the memory, how can I select only a subset of features from each example?
If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this?
Thanks
It seem... | [
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0.3328417242,
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0.0687172934,
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0.2765877843,
0.1568151861,
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0.0244091898,
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0.2368058562,
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-0.09436... |
https://github.com/huggingface/datasets/issues/1774 | is it possible to make slice to be more compatible like python list and numpy? | Hi ! Thanks for reporting.
I am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.
If you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)
I'll make a PR in a few days | Hi,
see below error:
```
AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples.
``` | 62 | is it possible to make slice to be more compatible like python list and numpy?
Hi,
see below error:
```
AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples.
```
Hi ! Thanks for reporting.
I am working on changes in the way data are sliced from arrow. I can probably fix your issue... | [
0.0045193993,
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0.1064820215,
0.2514835298,
0.120... |
https://github.com/huggingface/datasets/issues/1773 | bug in loading datasets | Looks like an issue with your csv file. Did you use the right delimiter ?
Apparently at line 37 the CSV reader from pandas reads 2 fields instead of 1. | Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
prin... | 30 | bug in loading datasets
Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 's... | [
-0.2842664719,
-0.271476686,
-0.1463157535,
0.4608482122,
0.3019686639,
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0.5121126771,
0.0764497593,
0.0585812256,
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0.0352055877,
-0.0331644267,
0.1680261642,
-0.0946209356,
0.01... |
https://github.com/huggingface/datasets/issues/1773 | bug in loading datasets | Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do
```python
from datasets import load_dataset
dataset = load_dataset('csv', data_files=data_files, sep="\t")
```
for example to use a tab separator.
You can see the full list of arguments ... | Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 'sick/validation.csv'})
prin... | 64 | bug in loading datasets
Hi,
I need to load a dataset, I use these commands:
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files={'train': 'sick/train.csv',
'test': 'sick/test.csv',
'validation': 's... | [
-0.2842664719,
-0.271476686,
-0.1463157535,
0.4608482122,
0.3019686639,
0.2408370525,
0.0834565386,
0.5121126771,
0.0764497593,
0.0585812256,
-0.0004540475,
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0.2536352575,
-0.0337030478,
0.0352055877,
-0.0331644267,
0.1680261642,
-0.0946209356,
0.01... |
https://github.com/huggingface/datasets/issues/1771 | Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py | Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.
See PR #1726
We'll do a new release soon :) | Hi,
When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?
```
Traceback (most recent call last):
File "/home/tom/pyenv/pystory/lib/python3.6/site-p... | 36 | Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
Hi,
When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset... | [
-0.0955532715,
-0.2019754946,
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0.1859904528,
0.2537336946,
0.2542535067,
0.0892186016,
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0.0940235183,
0.2848535478,
0.005462911,
0.0613933541,
0.1827713847,
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0.07383... |
https://github.com/huggingface/datasets/issues/1770 | how can I combine 2 dataset with different/same features? | Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)
For now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188 | to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} | 45 | how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
Hi ! Currently we don't have a way to `zip` datasets but we p... | [
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-0.4964472353,
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0.1217598021,
0.0697284117,
0.3581108749,
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0.1613288075,
-0.2478820235,
0.0789491534,
0.1294562817,
-0.4716951847,
0.198... |
https://github.com/huggingface/datasets/issues/1770 | how can I combine 2 dataset with different/same features? | Good to hear.
Currently I did not use map , just fetch src and tgt from the 2 dataset and merge them.
It will be a release if you can deal with it at the backend.
Thanks. | to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} | 37 | how can I combine 2 dataset with different/same features?
to combine 2 dataset by one-one map like ds = zip(ds1, ds2):
ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'}
or different feature:
ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
Good to hear.
Currently I did not use map , just fetch src a... | [
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-0.4940946698,
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0.0185404755,
-0.0224655606,
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0.0745650232,
0.1688350588,
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0.177... |
https://github.com/huggingface/datasets/issues/1769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ? | It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chine... | 21 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github... | [
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0.1661586612,
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0.2303638011,
0.2993956208,
0.2801508307,
0.0167169757,
0.3376861513,
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0.0041830107,
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-0.3830085397,
0.010605... |
https://github.com/huggingface/datasets/issues/1769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | > Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?
python==3.6.10
datasets==1.2.1
dill==0.3.2
pickle.format_version==4.0 | It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chine... | 26 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github... | [
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0.113244459,
0.1903710514,
0.1661586612,
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0.332113564,
0.2303638011,
0.2993956208,
0.2801508307,
0.0167169757,
0.3376861513,
-0.111044623,
0.2174143046,
0.0041830107,
-0.1060651168,
-0.1158020496,
-0.1348112375,
-0.3830085397,
0.010605... |
https://github.com/huggingface/datasets/issues/1769 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 | Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.
However objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.
Can you try to update your python version to python>=3.7 ?
| It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py
Script args:
```
--model_name_or_path
../../../model/chine... | 41 | _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.
The script I use is https://github... | [
-0.2903596759,
-0.297428906,
0.113244459,
0.1903710514,
0.1661586612,
-0.0522101186,
0.332113564,
0.2303638011,
0.2993956208,
0.2801508307,
0.0167169757,
0.3376861513,
-0.111044623,
0.2174143046,
0.0041830107,
-0.1060651168,
-0.1158020496,
-0.1348112375,
-0.3830085397,
0.010605... |
https://github.com/huggingface/datasets/issues/1766 | Issues when run two programs compute the same metrics | Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace "default_experiment" with the experiment id that you provide in the arrow filename.
Also when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure w... | I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch... | 69 | Issues when run two programs compute the same metrics
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
``... | [
-0.3611739874,
-0.1505348682,
-0.0431875437,
0.3872226477,
0.2670592666,
-0.1068402156,
0.0815508589,
0.3045550883,
-0.1409018636,
0.2166145891,
-0.3760762811,
-0.0134596685,
0.0852671862,
0.0051950905,
-0.1859993935,
-0.0460624062,
0.0034710031,
-0.0094137117,
0.0772824436,
-0... |
https://github.com/huggingface/datasets/issues/1766 | Issues when run two programs compute the same metrics | Thank you for your response. I fixed the issue by set "keep_in_memory=True" when load_metric.
I cannot share the entire source code but below is the wrapper I wrote:
```python
class Evaluation:
def __init__(self, metric='sacrebleu'):
# self.metric = load_metric(metric, keep_in_memory=True)
... | I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
```
File "train_matching_min.py", line 160, in <module>ch... | 94 | Issues when run two programs compute the same metrics
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches:
``... | [
-0.3611739874,
-0.1505348682,
-0.0431875437,
0.3872226477,
0.2670592666,
-0.1068402156,
0.0815508589,
0.3045550883,
-0.1409018636,
0.2166145891,
-0.3760762811,
-0.0134596685,
0.0852671862,
0.0051950905,
-0.1859993935,
-0.0460624062,
0.0034710031,
-0.0094137117,
0.0772824436,
-0... |
https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | Instead of:
```python
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)
```
It should be:
```python
dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)
```
`batch_sampler` accepts a Sampler object or an Iterable, so you get an error. | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 2... | 30 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tenso... | [
-0.2379292995,
0.055456318,
-0.0456709191,
0.2057333589,
0.1531802714,
0.0001413128,
0.7921317816,
0.3068522513,
0.0211306568,
0.1527127177,
0.0467957295,
0.2304262072,
-0.2809543908,
-0.2416440994,
-0.0902263597,
-0.0604500175,
-0.0764537454,
0.0790704489,
-0.1224218458,
0.032... |
https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | @mariosasko I thought that would fix it, but now I'm getting a different error:
```
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly no... | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 2... | 169 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tenso... | [
-0.2379292995,
0.055456318,
-0.0456709191,
0.2057333589,
0.1531802714,
0.0001413128,
0.7921317816,
0.3068522513,
0.0211306568,
0.1527127177,
0.0467957295,
0.2304262072,
-0.2809543908,
-0.2416440994,
-0.0902263597,
-0.0604500175,
-0.0764537454,
0.0790704489,
-0.1224218458,
0.032... |
https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | Yes, padding is an answer.
This can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 2... | 23 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tenso... | [
-0.2379292995,
0.055456318,
-0.0456709191,
0.2057333589,
0.1531802714,
0.0001413128,
0.7921317816,
0.3068522513,
0.0211306568,
0.1527127177,
0.0467957295,
0.2304262072,
-0.2809543908,
-0.2416440994,
-0.0902263597,
-0.0604500175,
-0.0764537454,
0.0790704489,
-0.1224218458,
0.032... |
https://github.com/huggingface/datasets/issues/1765 | Error iterating over Dataset with DataLoader | dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)
batch = next(iter(dataloader))
getting
ValueError: cannot reshape array of size 8192 into shape (1,512,4)
I had put padding as 2048 for encoded_dataset
kindly help | I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 2... | 28 | Error iterating over Dataset with DataLoader
I have a Dataset that I've mapped a tokenizer over:
```
encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])
encoded_dataset[:1]
```
```
{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
'input_ids': tenso... | [
-0.2379292995,
0.055456318,
-0.0456709191,
0.2057333589,
0.1531802714,
0.0001413128,
0.7921317816,
0.3068522513,
0.0211306568,
0.1527127177,
0.0467957295,
0.2304262072,
-0.2809543908,
-0.2416440994,
-0.0902263597,
-0.0604500175,
-0.0764537454,
0.0790704489,
-0.1224218458,
0.032... |
https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Hi ! You can get CUDA tensors with
```python
dataset.set_format("torch", columns=columns, device="cuda")
```
Indeed `set_format` passes the `**kwargs` to `torch.tensor` | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't suppor... | 20 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
... | [
-0.2256218493,
-0.4427933097,
-0.0796789601,
0.1839717627,
0.5433500409,
0.3885731101,
0.5336819887,
0.3345811069,
-0.0096711563,
0.1427577734,
-0.0833447576,
0.2423237413,
-0.2235850543,
0.0973635241,
0.1539928466,
-0.3738491833,
0.2507271171,
-0.1545419246,
-0.0661571473,
0.0... |
https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Hi @lhoestq,
Thanks a lot. Is this true for all format types?
As in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'? | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't suppor... | 38 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
... | [
-0.1914240569,
-0.4953544438,
-0.0635153577,
0.1430508196,
0.5737581253,
0.3578243256,
0.6109963655,
0.3751017153,
-0.0064690006,
0.0933136046,
-0.1843089908,
0.2385639697,
-0.1888937503,
0.2082742751,
0.185547173,
-0.3550988734,
0.2348215282,
-0.1543386877,
-0.0376627445,
0.07... |
https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.
We don't support the kwargs for pandas on the other hand. | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't suppor... | 26 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
... | [
-0.2339966446,
-0.4713831842,
-0.0587258562,
0.1454276443,
0.5945416093,
0.3813998997,
0.5536248684,
0.3486230075,
0.0204018541,
0.0847376511,
-0.13873972,
0.3407217562,
-0.1757795662,
0.1476909369,
0.1723190397,
-0.3778468966,
0.3084686697,
-0.0880435109,
-0.0487243719,
0.0729... |
https://github.com/huggingface/datasets/issues/1762 | Unable to format dataset to CUDA Tensors | Thanks @lhoestq,
Would it be okay if I added this to the docs and made a PR? | Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
I tried this, but Dataset doesn't suppor... | 17 | Unable to format dataset to CUDA Tensors
Hi,
I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.
... | [
-0.2076083869,
-0.4493274391,
-0.0808781311,
0.1429592818,
0.567246139,
0.3796425462,
0.5310694575,
0.3309663534,
-0.0181648768,
0.1409580857,
-0.0788681433,
0.2365305573,
-0.2230985761,
0.1325980276,
0.1703114957,
-0.3306936026,
0.2573103011,
-0.0979153141,
-0.0379617885,
0.07... |
https://github.com/huggingface/datasets/issues/1759 | wikipedia dataset incomplete | Hi !
From what pickle file fo you get this ?
I guess you mean the dataset loaded using `load_dataset` ? | Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants a... | 21 | wikipedia dataset incomplete
Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-... | [
0.1241123378,
0.0906562135,
-0.0648523346,
0.4390088022,
-0.1451848894,
0.1351811439,
0.1400449425,
-0.0993285626,
0.3315920532,
0.1742717028,
0.1878571659,
-0.0555861592,
0.3833339512,
-0.3714289665,
0.0213763453,
-0.2188197523,
0.1357133538,
0.1867663413,
-0.353669554,
-0.299... |
https://github.com/huggingface/datasets/issues/1759 | wikipedia dataset incomplete | yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. | Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants a... | 28 | wikipedia dataset incomplete
Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-... | [
0.0643154606,
0.0894950107,
-0.0597211011,
0.4419094324,
-0.1323367804,
0.1083994061,
0.1577169299,
-0.1221324801,
0.2750076056,
0.1589433104,
0.173834309,
-0.1358432323,
0.4038768113,
-0.4070718288,
0.0144942477,
-0.2074820846,
0.1505153775,
0.1527746618,
-0.3166954219,
-0.302... |
https://github.com/huggingface/datasets/issues/1759 | wikipedia dataset incomplete | The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell
There also exist other datasets based on wikipedia that were processed differently (and are ofte... | Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-sur-Ouche has 128 inhabitants a... | 48 | wikipedia dataset incomplete
Hey guys,
I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset.
Unfortunately, I found out that there is an incompleteness for the German dataset.
For reasons unknown to me, the number of inhabitants has been removed from many pages:
Thorey-... | [
0.1457833946,
0.165130645,
-0.0604216233,
0.4512960315,
-0.1779640764,
0.1422894001,
0.1325684041,
-0.0599783808,
0.2211329639,
0.1373943835,
0.1483721733,
-0.0644867122,
0.4038361907,
-0.4707551599,
-0.0247412547,
-0.1964902431,
0.1687638164,
0.1441212744,
-0.3371715844,
-0.28... |
https://github.com/huggingface/datasets/issues/1758 | dataset.search() (elastic) cannot reliably retrieve search results | Hi !
I tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.
Maybe this is because the index is not updated yet on the ElasticSearch side ? | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data t... | 41 | dataset.search() (elastic) cannot reliably retrieve search results
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run ... | [
0.2319183797,
-0.0181093998,
-0.1212054417,
0.1662060171,
0.1539811045,
-0.3275493681,
-0.1240266562,
0.1151944697,
-0.2562257349,
0.2235437483,
-0.09199889,
-0.0185619444,
-0.0620764457,
-0.5503269434,
-0.2119903266,
0.0474042371,
-0.1246100962,
0.1536927521,
0.0321474522,
-0.... |
https://github.com/huggingface/datasets/issues/1758 | dataset.search() (elastic) cannot reliably retrieve search results | Thanks for the feedback! I added a 30 second "sleep" and that seemed to work well! | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data t... | 16 | dataset.search() (elastic) cannot reliably retrieve search results
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run ... | [
0.2319183797,
-0.0181093998,
-0.1212054417,
0.1662060171,
0.1539811045,
-0.3275493681,
-0.1240266562,
0.1151944697,
-0.2562257349,
0.2235437483,
-0.09199889,
-0.0185619444,
-0.0620764457,
-0.5503269434,
-0.2119903266,
0.0474042371,
-0.1246100962,
0.1536927521,
0.0321474522,
-0.... |
https://github.com/huggingface/datasets/issues/1757 | FewRel | @dspoka Please check the following link : https://github.com/thunlp/FewRel
This link mentions two versions of the datasets. Also, this one seems to be the official link.
I am assuming this is the correct link and implementing based on the same. | ## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
auth... | 39 | FewRel
## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
... | [
-0.1948350221,
-0.0344231725,
-0.1016145349,
0.1268532425,
-0.0582779981,
-0.125049293,
0.4315412939,
0.1708206832,
0.1060706675,
0.1873809695,
-0.4312258661,
0.0766160786,
-0.0305744056,
-0.3551292121,
0.2079174519,
-0.1194451377,
-0.0137297492,
0.2213973552,
0.0144301234,
-0.... |
https://github.com/huggingface/datasets/issues/1755 | Using select/reordering datasets slows operations down immensely | Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`
```
examples = examples.flatten_indices()
features = features.flatten_indices()
```
That helped quite a bit! | I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour.
The below examp... | 39 | Using select/reordering datasets slows operations down immensely
I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples wo... | [
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0.1460768282,
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0.0000032496,
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... |
https://github.com/huggingface/datasets/issues/1747 | datasets slicing with seed | Hi :)
The slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.
You can shuffle and then take a subset of your dataset with
```python
# shuffle and take the first 100 examples
dataset = dataset.shuffle(seed=42).select(range(100))
```
You can find more information about sh... | Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq | 50 | datasets slicing with seed
Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq
Hi :)
The slicing API from h... | [
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0.123708643,
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https://github.com/huggingface/datasets/issues/1747 | datasets slicing with seed | thank you so much
On Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com>
wrote:
> Hi :)
> The slicing API doesn't shuffle the data.
> You can shuffle and then take a subset of your dataset with
>
> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))
>
> Yo... | Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq | 103 | datasets slicing with seed
Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq
thank you so much
On Mon, Jan... | [
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0.3337328732,
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0.6808799505,
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0.0889166296,
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0.2540715337,
-0.... |
https://github.com/huggingface/datasets/issues/1745 | difference between wsc and wsc.fixed for superglue | From the description given in the dataset script for `wsc.fixed`:
```
This version fixes issues where the spans are not actually substrings of the text.
``` | Hi
I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq | 26 | difference between wsc and wsc.fixed for superglue
Hi
I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq
From the description given in the dataset script for `wsc.fixed`:
```
This version... | [
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0.1931587756,
0.0983611122,
-0.2450374514,
0.3370180726,
-0.029704906,
0.2183906585,
-0.0... |
https://github.com/huggingface/datasets/issues/1743 | Issue while Creating Custom Metric | Currently it's only possible to define the features for the two columns `references` and `predictions`.
The data for these columns can then be passed to `metric.add_batch` and `metric.compute`.
Instead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predic... | Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
... | 151 | Issue while Creating Custom Metric
Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will ap... | [
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0.1864611059,
0.4193514287,
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0.1982862502,
0.0518294983,
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-0.3350708485,
0.1471370459,
0.3345439732,
-0.0... |
https://github.com/huggingface/datasets/issues/1743 | Issue while Creating Custom Metric | Hi @lhoestq,
I am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.
Including them in references seems like a good idea. I'll try it out and get back to you. If... | Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
... | 75 | Issue while Creating Custom Metric
Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will ap... | [
-0.2388353348,
-0.3020202518,
-0.1457838863,
0.1864611059,
0.4193514287,
-0.0384189971,
0.1069304571,
0.1982862502,
0.0518294983,
0.299782902,
-0.0557400435,
0.2383319736,
-0.1781436652,
0.1406404972,
-0.0143400328,
-0.1415148228,
-0.3350708485,
0.1471370459,
0.3345439732,
-0.0... |
https://github.com/huggingface/datasets/issues/1733 | connection issue with glue, what is the data url for glue? | Hello @juliahane, which config of GLUE causes you trouble?
The URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py | Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks | 20 | connection issue with glue, what is the data url for glue?
Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks
Hello @juliahane, whi... | [
-0.0552914403,
0.0118693495,
-0.0427739918,
0.2993859947,
0.3010599017,
-0.2684806287,
0.2035406679,
0.0815815851,
0.1122900471,
0.1263365895,
0.064289175,
-0.0648154169,
0.2632437348,
0.1945553571,
0.2005842328,
-0.0129128052,
-0.1107107103,
-0.0857952461,
-0.3790934086,
0.069... |
https://github.com/huggingface/datasets/issues/1731 | Couldn't reach swda.py | Hi @yangp725,
The SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.
You can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets... | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
| 54 | Couldn't reach swda.py
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
Hi @yangp725,
The SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.
You can still access it by... | [
-0.2362144887,
-0.3874822259,
-0.15831846,
-0.0046996097,
0.3615032434,
-0.0226997025,
-0.0685168654,
0.2251033485,
0.0263327397,
0.2184049785,
-0.1205208823,
-0.0072957999,
0.0098906429,
0.3339483142,
-0.0140379285,
-0.2254327983,
0.0509143099,
0.0313129015,
-0.2564316988,
0.0... |
https://github.com/huggingface/datasets/issues/1729 | Is there support for Deep learning datasets? | Hi @ZurMaD!
Thanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617
MNIST is also on the way: #1730
If you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/b... | I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets | 55 | Is there support for Deep learning datasets?
I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets
Hi @ZurMaD!
Thanks for your interest in 🤗 `datasets`. Su... | [
-0.2464447618,
-0.073237747,
-0.2349894494,
-0.0977335721,
0.2804594934,
0.0302641187,
0.2250184864,
0.0864043236,
0.0842461586,
0.2471519411,
-0.0170773175,
0.0281186439,
-0.3046903312,
0.3419504166,
0.2932602167,
-0.1668529958,
0.3030383885,
0.0297716595,
0.1124467254,
-0.188... |
https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | Hi @ameet-1997,
I think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets
For your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenat... | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training s... | 43 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print t... | [
0.1183494925,
0.12276797,
-0.0548611805,
-0.02185384,
0.1973935366,
0.30988276,
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-0.0837009549,
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0.2646121085,
0.5609713197,
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0.0916977674,
0.3351855278,
-0.2962876558,
0.1142138541,
0.0437107496,
-0.29227525,
-0.027993066... |
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