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test_fine_flow

Text Ranking
sentence-transformers
Safetensors
xlm-roberta
cross-encoder
reranker
Generated from Trainer
dataset_size:2879
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
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test_fine_flow / README.md
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metadata 
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:2879
  - loss:BinaryCrossEntropyLoss
base_model: BAAI/bge-reranker-v2-m3
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 BAAI/bge-reranker-v2-m3
    results:
      - task:
          type: cross-encoder-classification
          name: Cross Encoder Classification
        dataset:
          name: entity matching
          type: entity-matching
        metrics:
          - type: accuracy
            value: 0.8525121555915721
            name: Accuracy
          - type: accuracy_threshold
            value: 0.44037526845932007
            name: Accuracy Threshold
          - type: f1
            value: 0.8783068783068781
            name: F1
          - type: f1_threshold
            value: 0.3608097732067108
            name: F1 Threshold
          - type: precision
            value: 0.827930174563591
            name: Precision
          - type: recall
            value: 0.9352112676056338
            name: Recall
          - type: average_precision
            value: 0.9356992398880613
            name: Average Precision

CrossEncoder based on BAAI/bge-reranker-v2-m3

This is a Cross Encoder model finetuned from BAAI/bge-reranker-v2-m3 using the sentence-transformers 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: BAAI/bge-reranker-v2-m3
  • Maximum Sequence Length: 64 tokens
  • Number of Output Labels: 1 label

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("pujithapsx/test_fine_flow")
# Get scores for pairs of texts
pairs = [
    ['Yamini Durga Fernandes', 'Roy Yamini Durga'],
    ['C/O Ramesh Yadav Village Bairiya Post Bairiya Ballia', 'Village Bairiya C/O Ramesh Yadav Post Bairiya Ballia'],
    ['Flat 5 Lotus Tower Brigade Road Bengaluru', 'Flat 6 Lotus Tower Brigade Road Bangalore'],
    ['House 7 Tinsukia Village Post Tinsukia Assam Assam', 'Tinsukia Village Assam'],
    ['Rudra', 'Rudhraa'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Yamini Durga Fernandes',
    [
        'Roy Yamini Durga',
        'Village Bairiya C/O Ramesh Yadav Post Bairiya Ballia',
        'Flat 6 Lotus Tower Brigade Road Bangalore',
        'Tinsukia Village Assam',
        'Rudhraa',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Metric Value
accuracy 0.8525
accuracy_threshold 0.4404
f1 0.8783
f1_threshold 0.3608
precision 0.8279
recall 0.9352
average_precision 0.9357

Training Details

Training Dataset

Unnamed Dataset

  • Size: 2,879 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 label
    type string string int
    details
    • min: 4 characters
    • mean: 30.29 characters
    • max: 90 characters
    • min: 3 characters
    • mean: 31.45 characters
    • max: 106 characters
    • 0: ~42.10%
    • 1: ~57.90%
  • Samples:
    sentence1 sentence2 label
    Village Buxar Bihar Village Buxar Rohtas Bihar 0
    Dhruv Dhruvi 0
    Venkat Prakash Verma Venkat P Verma 1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Evaluation Dataset

Unnamed Dataset

  • Size: 617 evaluation samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 617 samples:
    sentence1 sentence2 label
    type string string int
    details
    • min: 4 characters
    • mean: 30.88 characters
    • max: 98 characters
    • min: 4 characters
    • mean: 31.67 characters
    • max: 100 characters
    • 0: ~42.46%
    • 1: ~57.54%
  • Samples:
    sentence1 sentence2 label
    Yamini Durga Fernandes Roy Yamini Durga 0
    C/O Ramesh Yadav Village Bairiya Post Bairiya Ballia Village Bairiya C/O Ramesh Yadav Post Bairiya Ballia 1
    Flat 5 Lotus Tower Brigade Road Bengaluru Flat 6 Lotus Tower Brigade Road Bangalore 0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 256
  • per_device_eval_batch_size: 32
  • learning_rate: 2e-05
  • weight_decay: 0.01
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • use_cpu: True
  • bf16: True
  • half_precision_backend: cpu_amp
  • load_best_model_at_end: True
  • dataloader_pin_memory: False

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 256
  • 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: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 1
  • 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: True
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • bf16: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: cpu_amp
  • 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: False
  • 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: {}

Training Logs

Epoch Step Validation Loss entity-matching_average_precision
0.1667 2 0.4423 0.9298
0.3333 4 0.4188 0.9319
0.5 6 0.4032 0.9335
0.6667 8 0.3935 0.9345
0.8333 10 0.3874 0.9353
1.0 12 0.3849 0.9357
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 5.3.0
  • Transformers: 4.57.6
  • PyTorch: 2.10.0+cu128
  • Accelerate: 1.13.0
  • Datasets: 4.8.4
  • Tokenizers: 0.22.2

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",
}