Add new SentenceTransformer model

b2eefc1 verified about 1 year ago

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:16000
  - loss:DenoisingAutoEncoderLoss
base_model: google-bert/bert-base-uncased
widget:
  - source_sentence: can so hopeless to who cares
    sentences:
      - >-
        id done that though it kind of did a on me and i found myself
        sympathizing with the demons as the church called them and feeling more
        disgusted with the people who were supposed to be trying to fight them
        off
      - >-
        i can go from feeling so hopeless to so damned hopeful just from being
        around someone who cares and is awake
      - >-
        i feel quite honored to exhibit my work in portugal especially within
        the critical and philosophical context of the god factor project said
        west
  - source_sentence: >-
      im feeling regretful not back i exact things you i would also to you
      letters
    sentences:
      - >-
        i feel like people dont really want me in their company but also they
        dont want to hurt my feelings
      - >-
        i continue to succeed in something and having someone seems unattainable
        because i feel men will be intimidated or when there is a prolonged
        moment of silence
      - >-
        im feeling regretful about not writing back to you i felt the exact same
        things you did and i would have also loved to have you read my letters
  - source_sentence: feel there not because or gary feel i moving them
    sentences:
      - >-
        i feel so unwelcome there but not because of her or gary i just feel
        that i shouldnt be moving back in with them
      - >-
        i dont know why but every time i feel like i am doing someone a favor
        all the time i start to feel burdened and stressed by that
      - >-
        id have spent more time with her on reading i feel a bit guilty about
        that
  - source_sentence: came diy twiggy holder feeling all and
    sentences:
      - i watch movies set in the s and s i feel pangs of melancholy
      - >-
        i came across this picture of a diy twiggy candle holder and now im
        feeling all festive and creative
      - >-
        i read other peoples posts there are moments where i feel id give my
        left fingernail to be them my left fingernail is precious because its
        the only one i can polish perfectly out of the
  - source_sentence: i missed precious summer
    sentences:
      - i feel so frightened i just wanted to document the way i m feeling
      - >-
        i really feel like i have a lot to offer in this area i would like to
        focus on troubled teenagers
      - i feel like i missed most of my precious summer
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on google-bert/bert-base-uncased

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: google-bert/bert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("KiViDrag/pretrain_emotion2")
# Run inference
sentences = [
    'i missed precious summer',
    'i feel like i missed most of my precious summer',
    'i feel so frightened i just wanted to document the way i m feeling',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 16,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 3 tokens
mean: 10.02 tokens
max: 36 tokens

min: 6 tokens
mean: 22.09 tokens
max: 72 tokens

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 10.02 tokens max: 36 tokens	min: 6 tokens mean: 22.09 tokens max: 72 tokens

Samples:

sentence_0	sentence_1
`i`	`i like to slump into when i m feeling precious`
`i say make anyone feel reaching their`	`i could say that will make anyone feel better than actually reaching their goal themselves`
`wont`	`i wont feel so damn idiotic`

Loss: DenoisingAutoEncoderLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 9
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 9
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
tp_size: 0
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
2.0	500	4.3707
4.0	1000	3.3926
6.0	1500	2.7636
8.0	2000	2.1161

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.50.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

DenoisingAutoEncoderLoss

@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}