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
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label

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': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

• Dataset: entity-matching-eval
• Evaluated with CrossEncoderClassificationEvaluator

Metric	Value
accuracy	0.9716
accuracy_threshold	0.0042
f1	0.974
f1_threshold	0.0042
precision	0.9615
recall	0.9868
average_precision	0.984

Training Details

Training Dataset

Unnamed Dataset

• Size: 981 training samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 981 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 8 characters mean: 27.85 characters max: 74 characters	min: 8 characters mean: 27.89 characters max: 62 characters	0: ~46.48% 1: ~53.52%

• Samples:

  sentence1	sentence2	label
  a-301 royal residency indore	a-301 royal res indore	1
  kukatpally hyderabad plot 45	plot 45 phase 2 kukatpally hyderabad	1
  a-301 royal residency indore	a-301 royal res indore	1

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 141 evaluation samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 141 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 10 characters mean: 27.66 characters max: 55 characters	min: 9 characters mean: 28.26 characters max: 51 characters	0: ~46.10% 1: ~53.90%

• Samples:

  sentence1	sentence2	label
  c/o gupta mg road indore	c/o gupta mg road ahmedabad	0
  d-101 sector 62 noida	d-102 sector 62 noida	0
  h.no 45-67 jayanagar bangalore	h.no 4567 jayanagar bangalore	1

• 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_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

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: 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: {'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: 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: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	entity-matching-eval_average_precision
0.1463	18	0.3776	-	-
0.2927	36	0.1942	0.5548	0.9583
0.4390	54	0.3252	-	-
0.5854	72	0.2161	0.3014	0.9740
0.7317	90	0.4467	-	-
0.8780	108	0.1705	0.1924	0.9899
1.0244	126	0.2846	-	-
1.1707	144	0.143	0.2629	0.9878
1.3171	162	0.1257	-	-
1.4634	180	0.1296	0.3058	0.9818
1.6098	198	0.1998	-	-
1.7561	216	0.0981	0.1660	0.9853
1.9024	234	0.1277	-	-
2.0488	252	0.0216	0.1888	0.9906
2.1951	270	0.1826	-	-
2.3415	288	0.0567	0.2956	0.9594
2.4878	306	0.0929	-	-
2.6341	324	0.0754	0.2090	0.9807
2.7805	342	0.0239	-	-
2.9268	360	0.0268	0.2494	0.9840

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",
}