pujithapsx/finetuned-bge-reranker-address-25l

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 <!-- at revision 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e -->
• —Maximum Sequence Length: 512 tokens
• —Number of Output Labels: 1 label <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Documentation: Cross Encoder Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Cross Encoders on Hugging Face

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/finetuned-bge-reranker-address-25l")
# Get scores for pairs of texts
pairs = [
    ['c/o gupta mg road indore', 'c/o gupta mg road ahmedabad'],
    ['d-101 sector 62 noida', 'd-102 sector 62 noida'],
    ['h.no 45-67 jayanagar bangalore', 'h.no 4567 jayanagar bangalore'],
    ['45 8th main indiranagar bangalore', 'indiranagar 45 8th main bangalore'],
    ['mvp colony visakhapatnam', 'mvp colony hyderabad'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'c/o gupta mg road indore',
    [
        'c/o gupta mg road ahmedabad',
        'd-102 sector 62 noida',
        'h.no 4567 jayanagar bangalore',
        'indiranagar 45 8th main bangalore',
        'mvp colony hyderabad',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Cross Encoder Classification

• —Dataset: entity-matching-eval
• —Evaluated with <code>CrossEncoderClassificationEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 981 training samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• —Approximate statistics based on the first 981 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 8 characters</li><li>mean: 27.85 characters</li><li>max: 74 characters</li></ul> | <ul><li>min: 8 characters</li><li>mean: 27.89 characters</li><li>max: 62 characters</li></ul> | <ul><li>0: ~46.48%</li><li>1: ~53.52%</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:------------------------------------------|:--------------------------------------------------|:---------------| | <code>a-301 royal residency indore</code> | <code>a-301 royal res indore</code> | <code>1</code> | | <code>kukatpally hyderabad plot 45</code> | <code>plot 45 phase 2 kukatpally hyderabad</code> | <code>1</code> | | <code>a-301 royal residency indore</code> | <code>a-301 royal res indore</code> | <code>1</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 141 evaluation samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• —Approximate statistics based on the first 141 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 10 characters</li><li>mean: 27.66 characters</li><li>max: 55 characters</li></ul> | <ul><li>min: 9 characters</li><li>mean: 28.26 characters</li><li>max: 51 characters</li></ul> | <ul><li>0: ~46.10%</li><li>1: ~53.90%</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:--------------------------------------------|:-------------------------------------------|:---------------| | <code>c/o gupta mg road indore</code> | <code>c/o gupta mg road ahmedabad</code> | <code>0</code> | | <code>d-101 sector 62 noida</code> | <code>d-102 sector 62 noida</code> | <code>0</code> | | <code>h.no 45-67 jayanagar bangalore</code> | <code>h.no 4567 jayanagar bangalore</code> | <code>1</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_eval_batch_size: 16
• —learning_rate: 2e-05
• —weight_decay: 0.01
• —warmup_steps: 36
• —remove_unused_columns: False
• —load_best_model_at_end: True

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: 16
• —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: 3
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_ratio: 0.0
• —warmup_steps: 36
• —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
• —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: False
• —label_names: None
• —load_best_model_at_end: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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: 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: 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: {}

</details>

Training Logs

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",
}

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