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---
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)
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## 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 |
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### Recommendations
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## 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",
}
```
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