| --- |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:11426 |
| - loss:BinaryCrossEntropyLoss |
| base_model: doctolib-lab/doctobert-fr-base |
| 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 doctolib-lab/doctobert-fr-base |
| results: |
| - task: |
| type: cross-encoder-classification |
| name: Cross Encoder Classification |
| dataset: |
| name: validation |
| type: validation |
| metrics: |
| - type: accuracy |
| value: 0.9871031746031746 |
| name: Accuracy |
| - type: accuracy_threshold |
| value: 0.5494990944862366 |
| name: Accuracy Threshold |
| - type: f1 |
| value: 0.9868819374369323 |
| name: F1 |
| - type: f1_threshold |
| value: 0.5494990944862366 |
| name: F1 Threshold |
| - type: precision |
| value: 0.9848942598187311 |
| name: Precision |
| - type: recall |
| value: 0.9888776541961577 |
| name: Recall |
| - type: average_precision |
| value: 0.9981127842050731 |
| name: Average Precision |
| --- |
| |
| # CrossEncoder based on doctolib-lab/doctobert-fr-base |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [doctolib-lab/doctobert-fr-base](https://huggingface.co/doctolib-lab/doctobert-fr-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:** [doctolib-lab/doctobert-fr-base](https://huggingface.co/doctolib-lab/doctobert-fr-base) <!-- at revision 314480d545f154e3758a1ce741ffe1ac4ac7b4d0 --> |
| - **Maximum Sequence Length:** 64 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': 'RobertaForSequenceClassification'}) |
| ) |
| ``` |
|
|
| ## 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("Fallovski/CrossencoderPharma") |
| # Get scores for pairs of inputs |
| pairs = [ |
| ['KLIPAL CODEINE 300/25 MG CP /16', 'ANTHELIOS 30+ ULT YX SENS 50ML'], |
| ['BIOTIC PLUS 100MG ENF 60ML', 'BIOTIC PLUS 500MG SACH B/14'], |
| ['FALCIART 80/480 MG COMP B/6', 'FALCIART CPR 80/480MG BT6'], |
| ['KLACIN CPR 625MG BT15', 'Klacin Cpr 625Mg Bt15'], |
| ['INDOCOLLYRE 0.1% ETO F/5ML B/1', 'Indocollyre 0.1% Eto F/5Ml B/1'], |
| ] |
| scores = model.predict(pairs) |
| print(scores) |
| # [9.7995e-04 1.1121e-03 9.9875e-01 9.9884e-01 9.9885e-01] |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| 'KLIPAL CODEINE 300/25 MG CP /16', |
| [ |
| 'ANTHELIOS 30+ ULT YX SENS 50ML', |
| 'BIOTIC PLUS 500MG SACH B/14', |
| 'FALCIART CPR 80/480MG BT6', |
| 'Klacin Cpr 625Mg Bt15', |
| 'Indocollyre 0.1% Eto F/5Ml B/1', |
| ] |
| ) |
| # [{'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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Cross Encoder Classification |
|
|
| * Dataset: `validation` |
| * Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator) |
|
|
| | Metric | Value | |
| |:----------------------|:-----------| |
| | accuracy | 0.9871 | |
| | accuracy_threshold | 0.5495 | |
| | f1 | 0.9869 | |
| | f1_threshold | 0.5495 | |
| | precision | 0.9849 | |
| | recall | 0.9889 | |
| | **average_precision** | **0.9981** | |
| |
| <!-- |
| ## 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: 11,426 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence1 | sentence2 | label | |
| |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | modality | text | text | | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 16.49 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.99 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:--------------------------------------------------|:-------------------------------------------------|:-----------------| |
| | <code>SÉROLOGIE ARBOVIRUS</code> | <code>SÉROLOGIE WRIGHT</code> | <code>0.0</code> | |
| | <code>SOND FOL H 15ML AA14 16CH /1</code> | <code>SOND FOL H 15ML AA14 16CH 1</code> | <code>1.0</code> | |
| | <code>FARLINE CAPRI D +1.5 MARON BLANC HOM</code> | <code>FARLINE MONZA CAREY +3.0 MARON/NOIR</code> | <code>0.0</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 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 2,016 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence1 | sentence2 | label | |
| |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | modality | text | text | | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 16.54 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.98 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:---------------------------------------------|:--------------------------------------------|:-----------------| |
| | <code>KLIPAL CODEINE 300/25 MG CP /16</code> | <code>ANTHELIOS 30+ ULT YX SENS 50ML</code> | <code>0.0</code> | |
| | <code>BIOTIC PLUS 100MG ENF 60ML</code> | <code>BIOTIC PLUS 500MG SACH B/14</code> | <code>0.0</code> | |
| | <code>FALCIART 80/480 MG COMP B/6</code> | <code>FALCIART CPR 80/480MG BT6</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 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 6 |
| - `warmup_ratio`: 0.1 |
| - `load_best_model_at_end`: 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`: 2e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 6 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: 0.1 |
| - `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`: True |
| - `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`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | validation_average_precision | |
| |:-------:|:-------:|:-------------:|:---------------:|:----------------------------:| |
| | 0.1397 | 50 | 0.6869 | - | - | |
| | 0.2793 | 100 | 0.3572 | - | - | |
| | 0.4190 | 150 | 0.1483 | - | - | |
| | 0.5587 | 200 | 0.13 | - | - | |
| | 0.6983 | 250 | 0.1394 | - | - | |
| | 0.8380 | 300 | 0.106 | - | - | |
| | 0.9777 | 350 | 0.0999 | - | - | |
| | 1.0 | 358 | - | 0.1673 | 0.9963 | |
| | 1.1173 | 400 | 0.084 | - | - | |
| | 1.2570 | 450 | 0.0731 | - | - | |
| | 1.3966 | 500 | 0.0716 | - | - | |
| | 1.5363 | 550 | 0.0699 | - | - | |
| | 1.6760 | 600 | 0.0647 | - | - | |
| | 1.8156 | 650 | 0.0691 | - | - | |
| | 1.9553 | 700 | 0.0942 | - | - | |
| | **2.0** | **716** | **-** | **0.0571** | **0.9981** | |
| | 2.0950 | 750 | 0.0399 | - | - | |
| | 2.2346 | 800 | 0.0503 | - | - | |
| | 2.3743 | 850 | 0.0219 | - | - | |
| | 2.5140 | 900 | 0.0456 | - | - | |
| | 2.6536 | 950 | 0.0536 | - | - | |
| | 2.7933 | 1000 | 0.0349 | - | - | |
| | 2.9330 | 1050 | 0.052 | - | - | |
| | 3.0 | 1074 | - | 0.0602 | 0.9983 | |
| | 3.0726 | 1100 | 0.0398 | - | - | |
| | 3.2123 | 1150 | 0.0128 | - | - | |
| | 3.3520 | 1200 | 0.022 | - | - | |
| | 3.4916 | 1250 | 0.0122 | - | - | |
| | 3.6313 | 1300 | 0.0265 | - | - | |
| | 3.7709 | 1350 | 0.04 | - | - | |
| | 3.9106 | 1400 | 0.0242 | - | - | |
| | 4.0 | 1432 | - | 0.0684 | 0.9987 | |
| | 4.0503 | 1450 | 0.0186 | - | - | |
| | 4.1899 | 1500 | 0.0185 | - | - | |
| | 4.3296 | 1550 | 0.0129 | - | - | |
| | 4.4693 | 1600 | 0.0113 | - | - | |
| | 4.6089 | 1650 | 0.0103 | - | - | |
| | 4.7486 | 1700 | 0.003 | - | - | |
| | 4.8883 | 1750 | 0.0207 | - | - | |
| | 5.0 | 1790 | - | 0.0744 | 0.9987 | |
| | 5.0279 | 1800 | 0.0181 | - | - | |
| | 5.1676 | 1850 | 0.0158 | - | - | |
| | 5.3073 | 1900 | 0.0156 | - | - | |
| | 5.4469 | 1950 | 0.0123 | - | - | |
| | 5.5866 | 2000 | 0.0094 | - | - | |
| | 5.7263 | 2050 | 0.0159 | - | - | |
| | 5.8659 | 2100 | 0.0033 | - | - | |
| | 6.0 | 2148 | - | 0.0649 | 0.9988 | |
| | -1 | -1 | - | - | 0.9981 | |
| |
| * The bold row denotes the saved checkpoint. |
| |
| ### Training Time |
| - **Training**: 10.8 minutes |
| - **Evaluation**: 1.1 minutes |
| - **Total**: 11.9 minutes |
| |
| ### Framework Versions |
| - Python: 3.13.11 |
| - Sentence Transformers: 5.6.1 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.13.0+cu130 |
| - Accelerate: 1.14.0 |
| - Datasets: 5.0.1 |
| - Tokenizers: 0.22.2 |
| |
| ## 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.* |
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