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.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("kallilikhitha123/bge-reranker-name-finetuned-2403")
# Get scores for pairs of texts
pairs = [
    ['saumya anika', 'saumya avni'],
    ['l sankeerthana a', 'alavala lakshmi sankeerthana'],
    ['rahulgupta', 'mohit gupta'],
    ['anil singh', 'a singh'],
    ['lakshminarayana', 'satya narayana'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'saumya anika',
    [
        'saumya avni',
        'alavala lakshmi sankeerthana',
        'mohit gupta',
        'a singh',
        'satya narayana',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Dataset: entity-matching-eval
Evaluated with CrossEncoderClassificationEvaluator

Metric	Value
accuracy	0.9929
accuracy_threshold	0.587
f1	0.9947
f1_threshold	0.587
precision	1.0
recall	0.9894
average_precision	0.9998

Training Details

Training Dataset

Unnamed Dataset

Size: 986 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 986 samples:
sentence1 sentence2 label
type string string int
details
min: 4 characters
mean: 12.46 characters
max: 33 characters
min: 3 characters
mean: 12.08 characters
max: 33 characters
0: ~33.16%
1: ~66.84%
Samples:
sentence1 sentence2 label
varunjain varun jain 1
vijay singh s vijay 1
vijay singh vijay s 1
Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

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: 4 characters
mean: 12.34 characters
max: 30 characters
min: 4 characters
mean: 12.84 characters
max: 29 characters
0: ~33.33%
1: ~66.67%
Samples:
sentence1 sentence2 label
saumya anika saumya avni 0
l sankeerthana a alavala lakshmi sankeerthana 1
rahulgupta mohit gupta 0
Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_eval_batch_size: 16
gradient_accumulation_steps: 2
learning_rate: 1.5e-05
weight_decay: 0.02
warmup_steps: 12
remove_unused_columns: False
load_best_model_at_end: True

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 16
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1.5e-05
weight_decay: 0.02
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: None
warmup_steps: 12
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: False
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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
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
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_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
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
use_cache: False
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
0.6613	41	0.3345	-	-
1.3226	82	0.0929	-	-
1.9839	123	0.0331	0.0318	0.9998
2.6452	164	0.0325	-	-

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.3
Transformers: 5.0.0
PyTorch: 2.10.0+cpu
Accelerate: 1.12.0
Datasets: 4.8.3
Tokenizers: 0.22.2

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",
}

	sentence1	sentence2	label
type	string	string	int
details	min: 4 characters mean: 12.46 characters max: 33 characters	min: 3 characters mean: 12.08 characters max: 33 characters	0: ~33.16% 1: ~66.84%

sentence1	sentence2	label
`varunjain`	`varun jain`	`1`
`vijay singh`	`s vijay`	`1`
`vijay singh`	`vijay s`	`1`

sentence1	sentence2	label
`saumya anika`	`saumya avni`	`0`
`l sankeerthana a`	`alavala lakshmi sankeerthana`	`1`
`rahulgupta`	`mohit gupta`	`0`