---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:114138
- loss:BinaryCrossEntropyLoss
base_model: cross-encoder/ms-marco-MiniLM-L6-v2
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- accuracy
- accuracy_threshold
- f1
- f1_threshold
- precision
- recall
- average_precision
model-index:
- name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
results:
- task:
type: cross-encoder-binary-classification
name: Cross Encoder Binary Classification
dataset:
name: eval
type: eval
metrics:
- type: accuracy
value: 0.8988329916416969
name: Accuracy
- type: accuracy_threshold
value: 0.10371464490890503
name: Accuracy Threshold
- type: f1
value: 0.8317532549614461
name: F1
- type: f1_threshold
value: -0.45371487736701965
name: F1 Threshold
- type: precision
value: 0.7977691561590688
name: Precision
- type: recall
value: 0.8687615526802218
name: Recall
- type: average_precision
value: 0.9072097927185474
name: Average Precision
---
# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2)
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
['The item is a promotional display featuring a variety of phone cases, including solid blue cases, cases with artistic designs, and one showcasing a kitten wearing a Santa hat.', 'A black phone case.'],
['It was a black umbrella with a loop.', 'A new, mustard-yellow, waffle-knit long-sleeved henley shirt features a three-button placket, a chest pocket with a "Custom Supply" label, and an "L.O.G.G." tag at the neckline.'],
['A white sneaker with black, pink, and silver accents.', 'A blue backpack has an orange and white front with black straps.'],
['Oh, that sleek white TYESO tumbler with the silver top, I was just about to try it out for keeping my coffee warm all day.', 'It is a white, metal TYESO brand vacuum-insulated bottle/mug with a silver rim and a black lid with a clear straw.'],
['It is a bright orange backpack with a small pink strawberry charm.', 'The medium-sized black backpack, likely made of nylon or a similar synthetic material, features a white rectangular tag with "MUSIC IS POWER" printed on it and appears to be in good condition.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'The item is a promotional display featuring a variety of phone cases, including solid blue cases, cases with artistic designs, and one showcasing a kitten wearing a Santa hat.',
[
'A black phone case.',
'A new, mustard-yellow, waffle-knit long-sleeved henley shirt features a three-button placket, a chest pocket with a "Custom Supply" label, and an "L.O.G.G." tag at the neckline.',
'A blue backpack has an orange and white front with black straps.',
'It is a white, metal TYESO brand vacuum-insulated bottle/mug with a silver rim and a black lid with a clear straw.',
'The medium-sized black backpack, likely made of nylon or a similar synthetic material, features a white rectangular tag with "MUSIC IS POWER" printed on it and appears to be in good condition.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
## Evaluation
### Metrics
#### Cross Encoder Binary Classification
* Dataset: `eval`
* Evaluated with [CEBinaryClassificationEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEBinaryClassificationEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| accuracy | 0.8988 |
| accuracy_threshold | 0.1037 |
| f1 | 0.8318 |
| f1_threshold | -0.4537 |
| precision | 0.7978 |
| recall | 0.8688 |
| **average_precision** | **0.9072** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 114,138 training samples
* Columns: sentence_0, sentence_1, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 15 characters
- mean: 106.73 characters
- max: 361 characters
| - min: 14 characters
- mean: 110.94 characters
- max: 403 characters
| - min: 0.0
- mean: 0.3
- max: 1.0
|
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| The item is a promotional display featuring a variety of phone cases, including solid blue cases, cases with artistic designs, and one showcasing a kitten wearing a Santa hat. | A black phone case. | 0.0 |
| It was a black umbrella with a loop. | A new, mustard-yellow, waffle-knit long-sleeved henley shirt features a three-button placket, a chest pocket with a "Custom Supply" label, and an "L.O.G.G." tag at the neckline. | 0.0 |
| A white sneaker with black, pink, and silver accents. | A blue backpack has an orange and white front with black straps. | 0.0 |
* Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 3
- `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
- `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}
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}