First version of my trained model

README.md

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+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:87398
+- 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: 0.9715485242270209
+      name: F1 Macro
+    - type: f1_micro
+      value: 0.9743012183884509
+      name: F1 Micro
+    - type: f1_weighted
+      value: 0.974262256621189
+      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 аллергенов пыли № 1 IgE (домашняя пыль (Greer), клещ-дерматофаг перинный, клещ-дерматофаг мучной, таракан)', 'Смесь аллергенов пыли - hm1, Состав: домашняя пыль, Dermatophagoides pteronyssinus, Dermatophagoides farinae, таракан-прусак, IgE. Метод: ИФА'],
+    ['Жидкостная цитология РШМ', 'Жидкостная цитология. Исследование соскоба шейки матки и цервикального канала (окрашивание по Папаниколау)'],
+    ['Посев на возбудителей кишечной инфекции (сальмонеллы, шигеллы) с определением чувствительности к основному спектру антибиотиков', 'Посев кала на патогенную флору (дизентерийная и тифопаратифозная группы): С определением чувствительности к антибиотикам. Метод: культуральный'],
+    ['Молекулярно-генетическое исследование мутации в гене V617F (замена 617-ой аминокислоты с валина на фенилаланин) JAK2 (янус тирозин-киназа второго типа / Качественная оценка наличия соматической мутации V617F в 14 экзоне гена JAK2 (Qualitative assessment of presence of gene JAK2 617F somatic mutation)', 'Анализ мутации V617F гена JAK2 (замена валин на фенилаланин). Метод: ПЦР'],
+    ['Водородно-метановый дыхательный тест с лактулозой (СИБРТЕСТ, синдром избыточного бактериального роста в тонкой кишке, СИБР) (самостоятельное взятие проб)', 'Дыхательный водородный тест на СИБР'],
+]
+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** | **0.9715** |
+| f1_micro     | 0.9743     |
+| f1_weighted  | 0.9743     |
+
+<!--
+## 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: 87,398 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: 5 characters</li><li>mean: 64.98 characters</li><li>max: 553 characters</li></ul> | <ul><li>min: 6 characters</li><li>mean: 63.31 characters</li><li>max: 477 characters</li></ul> | <ul><li>0: ~34.40%</li><li>1: ~65.60%</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                   | sentence_1                                                                                                                                                  | label          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Панель аллергенов пыли № 1 IgE (домашняя пыль (Greer), клещ-дерматофаг перинный, клещ-дерматофаг мучной, таракан)</code>               | <code>Смесь аллергенов пыли - hm1, Состав: домашняя пыль, Dermatophagoides pteronyssinus, Dermatophagoides farinae, таракан-прусак, IgE. Метод: ИФА</code>  | <code>1</code> |
+  | <code>Жидкостная цитология РШМ</code>                                                                                                        | <code>Жидкостная цитология. Исследование соскоба шейки матки и цервикального канала (окрашивание по Папаниколау)</code>                                     | <code>1</code> |
+  | <code>Посев на возбудителей кишечной инфекции (сальмонеллы, шигеллы) с определением чувствительности к основному спектру антибиотиков</code> | <code>Посев кала на патогенную флору (дизентерийная и тифопаратифозная группы): С определением чувствительности к антибиотикам. Метод: культуральный</code> | <code>1</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 |
+|:------:|:-----:|:-------------:|:------------------------------:|
+| 0.0458 | 500   | 0.5378        | -                              |
+| 0.0915 | 1000  | 0.2207        | -                              |
+| 0.1373 | 1500  | 0.2019        | -                              |
+| 0.1831 | 2000  | 0.1981        | 0.9654                         |
+| 0.2288 | 2500  | 0.19          | -                              |
+| 0.2746 | 3000  | 0.1703        | -                              |
+| 0.3204 | 3500  | 0.217         | -                              |
+| 0.3661 | 4000  | 0.1673        | 0.9627                         |
+| 0.4119 | 4500  | 0.1739        | -                              |
+| 0.4577 | 5000  | 0.143         | -                              |
+| 0.5034 | 5500  | 0.1522        | -                              |
+| 0.5492 | 6000  | 0.1545        | 0.9703                         |
+| 0.5950 | 6500  | 0.1353        | -                              |
+| 0.6407 | 7000  | 0.1438        | -                              |
+| 0.6865 | 7500  | 0.1339        | -                              |
+| 0.7323 | 8000  | 0.1355        | 0.9715                         |
+| 0.7780 | 8500  | 0.155         | -                              |
+| 0.8238 | 9000  | 0.1256        | -                              |
+| 0.8696 | 9500  | 0.1266        | -                              |
+| 0.9153 | 10000 | 0.1027        | 0.9715                         |
+
+
+### 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.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.1.0"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 46166
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eaa9d42c7aee85075ba233436b79292b6c3d73124c723b567251112f5402373a
+size 1436163192

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "46165": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 8192,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}