| --- |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:31340 |
| - loss:BinaryCrossEntropyLoss |
| base_model: BAAI/bge-reranker-base |
| pipeline_tag: text-ranking |
| library_name: sentence-transformers |
| --- |
| |
| # CrossEncoder based on BAAI/bge-reranker-base |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) 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:** [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) <!-- at revision 2cfc18c9415c912f9d8155881c133215df768a70 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Number of Output Labels:** 1 label |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| CrossEncoder( |
| (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'}) |
| ) |
| ``` |
|
|
| ## 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 inputs |
| pairs = [ |
| ['حقيبة تشانك لوكس', "Globus Women's Textured Vegan Leather Sling Bag Tan | Globus Women Tan Vegan Leather Textured Sling Bag With Detachable Strap | globus | Crossbody Bags | Tan"], |
| ['حريمية ماسكات كورية', 'Kappa 3-Pack Crew Socks Multicolour | Kappa Pack of 3 Crew Length Socks | Kappa | Socks | Multicolour'], |
| ['شسي غير مبطنة', 'Fall In Love Unlined Bodysuit | فول إن لوف بودي سوت غير مبطن | DeFacto | Body Suits | Deep Magenta'], |
| ['كندرة رموش مريحة للستات', 'Lift N Snatch Brow Tint Pen Black | قلم تحديد الحواجب ليفت أند سناتش رمادي أسود | NYX PROFESSIONAL MAKEUP | All Products | Black'], |
| ['white blouse', '2Xtremz Schiffli Ruffle Cotton Top White | 2Xtremz Regular Fit Cotton Top with Schiffli and Ruffle Detail | 2Xtremz | Blouses | White'], |
| ] |
| scores = model.predict(pairs) |
| print(scores) |
| # [0.9418 0.0044 0.978 0.2881 0.9463] |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| 'حقيبة تشانك لوكس', |
| [ |
| "Globus Women's Textured Vegan Leather Sling Bag Tan | Globus Women Tan Vegan Leather Textured Sling Bag With Detachable Strap | globus | Crossbody Bags | Tan", |
| 'Kappa 3-Pack Crew Socks Multicolour | Kappa Pack of 3 Crew Length Socks | Kappa | Socks | Multicolour', |
| 'Fall In Love Unlined Bodysuit | فول إن لوف بودي سوت غير مبطن | DeFacto | Body Suits | Deep Magenta', |
| 'Lift N Snatch Brow Tint Pen Black | قلم تحديد الحواجب ليفت أند سناتش رمادي أسود | NYX PROFESSIONAL MAKEUP | All Products | Black', |
| '2Xtremz Schiffli Ruffle Cotton Top White | 2Xtremz Regular Fit Cotton Top with Schiffli and Ruffle Detail | 2Xtremz | Blouses | White', |
| ] |
| ) |
| # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 31,340 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:---------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | modality | text | text | | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 7.44 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 37.84 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.69</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:---------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
| | <code>حقيبة تشانك لوكس</code> | <code>Globus Women's Textured Vegan Leather Sling Bag Tan \| Globus Women Tan Vegan Leather Textured Sling Bag With Detachable Strap \| globus \| Crossbody Bags \| Tan</code> | <code>1.0</code> | |
| | <code>حريمية ماسكات كورية</code> | <code>Kappa 3-Pack Crew Socks Multicolour \| Kappa Pack of 3 Crew Length Socks \| Kappa \| Socks \| Multicolour</code> | <code>0.0</code> | |
| | <code>شسي غير مبطنة</code> | <code>Fall In Love Unlined Bodysuit \| فول إن لوف بودي سوت غير مبطن \| DeFacto \| Body Suits \| Deep Magenta</code> | <code>1.0</code> | |
| * Loss: [<code>BinaryCrossEntropyLoss</code>](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 |
|
|
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `fp16`: True |
| - `disable_tqdm`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `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 |
| - `use_ipex`: False |
| - `bf16`: False |
| - `fp16`: True |
| - `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`: True |
| - `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} |
| - `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`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.5102 | 500 | 0.6826 | |
| | 1.0204 | 1000 | 0.4261 | |
| | 1.5306 | 1500 | 0.3741 | |
| | 2.0408 | 2000 | 0.3523 | |
| | 2.5510 | 2500 | 0.33 | |
|
|
|
|
| ### Training Time |
| - **Training**: 5.3 minutes |
|
|
| ### Framework Versions |
| - Python: 3.11.12 |
| - Sentence Transformers: 5.5.1 |
| - Transformers: 4.49.0 |
| - PyTorch: 2.7.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.21.4 |
|
|
| ## Additional Resources |
|
|
| - [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): the end-to-end guide for training or finetuning Cross Encoder (reranker) models. |
| - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video reranker models through the same API. |
| - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal Cross Encoders. |
|
|
| ## Citation |
|
|
| ### BibTeX |
|
|
| #### Sentence Transformers |
| ```bibtex |
| @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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
