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CrossEncoderRanker

Text Classification
sentence-transformers
Safetensors
xlm-roberta
cross-encoder
reranker
Generated from Trainer
dataset_size:82796
loss:CrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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CrossEncoderRanker / README.md
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metadata 
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 model finetuned from deepvk/USER-bge-m3 using the sentence-transformers 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
  • Maximum Sequence Length: 8192 tokens
  • Number of Output Labels: 2 labels

Model Sources

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

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)

Evaluation

Metrics

Cross Encoder Softmax Accuracy

Metric Value
f1_macro 0.9772
f1_micro 0.9772
f1_weighted 0.9772

Training Details

Training Dataset

Unnamed Dataset

  • Size: 82,796 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string int
    details
    • min: 4 characters
    • mean: 66.18 characters
    • max: 504 characters
    • min: 3 characters
    • mean: 62.27 characters
    • max: 385 characters
    • 0: ~50.60%
    • 1: ~49.40%
  • Samples:
    sentence_0 sentence_1 label
    Панель №6 IgE (Сазан, карп, щука, судак, кефаль, ледяная рыба, пикша, осетр) Сазан, (Cyprinus carpio), IgE, аллерген - e82. Метод: ИФА 1
    Определение антител класса M (IgM) к цитомегаловирусу (CytomegАlovirus) в крови Бактериологическое исследование гнойного отделяемого: На аэробные и факультативно-анаэробные микроорганизмы. Метод: культуральный 0
    Исследования уровня бетта-изомеризованного C-концевого телопептида коллагена 1 типа (Beta-Cross laps) в крови Глюкоза, в венозной крови 0
  • Loss: CrossEntropyLoss

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • num_train_epochs: 1

All Hyperparameters

Click to expand
  • 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: {}

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 

@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",
}