| --- |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:82796 |
| - loss:CrossEntropyLoss |
| base_model: deepvk/USER-bge-m3 |
| pipeline_tag: text-classification |
| library_name: sentence-transformers |
| metrics: |
| - f1_macro |
| - f1_micro |
| - f1_weighted |
| model-index: |
| - name: CrossEncoder based on deepvk/USER-bge-m3 |
| results: |
| - task: |
| type: cross-encoder-softmax-accuracy |
| name: Cross Encoder Softmax Accuracy |
| dataset: |
| name: softmax accuracy eval |
| type: softmax_accuracy_eval |
| metrics: |
| - type: f1_macro |
| value: 0.9771728083627488 |
| name: F1 Macro |
| - type: f1_micro |
| value: 0.9771739130434782 |
| name: F1 Micro |
| - type: f1_weighted |
| value: 0.9771740511285696 |
| name: F1 Weighted |
| --- |
| |
| # CrossEncoder based on deepvk/USER-bge-m3 |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [deepvk/USER-bge-m3](https://huggingface.co/deepvk/USER-bge-m3) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text pair classification. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Cross Encoder |
| - **Base model:** [deepvk/USER-bge-m3](https://huggingface.co/deepvk/USER-bge-m3) <!-- at revision 0cc6cfe48e260fb0474c753087a69369e88709ae --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Number of Output Labels:** 2 labels |
| <!-- - **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/UKPLab/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("Chimalpopoka/CrossEncoderRanker") |
| # Get scores for pairs of texts |
| pairs = [ |
| ['Панель №6 IgE (Сазан, карп, щука, судак, кефаль, ледяная рыба, пикша, осетр)', 'Сазан, (Cyprinus carpio), IgE, аллерген - e82. Метод: ИФА'], |
| ['Определение антител класса M (IgM) к цитомегаловирусу (CytomegАlovirus) в крови', 'Бактериологическое исследование гнойного отделяемого: На аэробные и факультативно-анаэробные микроорганизмы. Метод: культуральный'], |
| ['Исследования уровня бетта-изомеризованного C-концевого телопептида коллагена 1 типа (Beta-Cross laps) в крови', 'Глюкоза, в венозной крови'], |
| ['Посев кала на диарогенные эшерихиозы (E. coli), закл., Кал', 'Коклюш (Bordetella pertussis): Антитела: IgG, (количественно). Метод: ИФА'], |
| ['Ультразвуковое исследование поджелудочной железы (детям)', 'УЗИ поджелудочной железы, для детей'], |
| ] |
| scores = model.predict(pairs) |
| print(scores.shape) |
| # (5, 2) |
| ``` |
|
|
| <!-- |
| ### 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 Softmax Accuracy |
|
|
| * Dataset: `softmax_accuracy_eval` |
| * Evaluated with [<code>CESoftmaxAccuracyEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CESoftmaxAccuracyEvaluator) |
|
|
| | Metric | Value | |
| |:-------------|:-----------| |
| | **f1_macro** | **0.9772** | |
| | f1_micro | 0.9772 | |
| | f1_weighted | 0.9772 | |
| |
| <!-- |
| ## 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: 82,796 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 4 characters</li><li>mean: 66.18 characters</li><li>max: 504 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 62.27 characters</li><li>max: 385 characters</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:---------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Панель №6 IgE (Сазан, карп, щука, судак, кефаль, ледяная рыба, пикша, осетр)</code> | <code>Сазан, (Cyprinus carpio), IgE, аллерген - e82. Метод: ИФА</code> | <code>1</code> | |
| | <code>Определение антител класса M (IgM) к цитомегаловирусу (CytomegАlovirus) в крови</code> | <code>Бактериологическое исследование гнойного отделяемого: На аэробные и факультативно-анаэробные микроорганизмы. Метод: культуральный</code> | <code>0</code> | |
| | <code>Исследования уровня бетта-изомеризованного C-концевого телопептида коллагена 1 типа (Beta-Cross laps) в крови</code> | <code>Глюкоза, в венозной крови</code> | <code>0</code> | |
| * Loss: [<code>CrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss) |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `num_train_epochs`: 1 |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 8 |
| - `per_device_eval_batch_size`: 8 |
| - `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`: 1 |
| - `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} |
| - `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 |
| - `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`: False |
| - `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`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | softmax_accuracy_eval_f1_macro | |
| |:------:|:-----:|:-------------:|:------------------------------:| |
| | 0.0483 | 500 | 0.5573 | - | |
| | 0.0966 | 1000 | 0.2189 | - | |
| | 0.1449 | 1500 | 0.2144 | - | |
| | 0.1932 | 2000 | 0.1876 | 0.9683 | |
| | 0.2415 | 2500 | 0.1812 | - | |
| | 0.2899 | 3000 | 0.1657 | - | |
| | 0.3382 | 3500 | 0.1796 | - | |
| | 0.3865 | 4000 | 0.1592 | 0.9702 | |
| | 0.4348 | 4500 | 0.156 | - | |
| | 0.4831 | 5000 | 0.1491 | - | |
| | 0.5314 | 5500 | 0.1555 | - | |
| | 0.5797 | 6000 | 0.1216 | 0.9683 | |
| | 0.6280 | 6500 | 0.1276 | - | |
| | 0.6763 | 7000 | 0.1305 | - | |
| | 0.7246 | 7500 | 0.1156 | - | |
| | 0.7729 | 8000 | 0.1197 | 0.9759 | |
| | 0.8213 | 8500 | 0.1215 | - | |
| | 0.8696 | 9000 | 0.1065 | - | |
| | 0.9179 | 9500 | 0.0896 | - | |
| | 0.9662 | 10000 | 0.1135 | 0.9772 | |
| |
| |
| ### Framework Versions |
| - Python: 3.12.3 |
| - Sentence Transformers: 5.1.0 |
| - Transformers: 4.53.2 |
| - PyTorch: 2.7.1+cu126 |
| - Accelerate: 1.10.1 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.21.2 |
| |
| ## 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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