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/finetuned-bge-reranker-2403")
# Get scores for pairs of texts
pairs = [
    ['bhargavi madhavi', 'bhargavi srinidhi'],
    ['navya kiara', 'navya tanvi'],
    ['manish', 'manisha'],
    ['anantha padmanabha', 'ananthpadmanabha'],
    ['nitin singh', 'n singh'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'bhargavi madhavi',
    [
        'bhargavi srinidhi',
        'navya tanvi',
        'manisha',
        'ananthpadmanabha',
        'n singh',
    ]
)
# [{'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	0.9984
f1	0.9762
f1_threshold	0.9965
precision	0.988
recall	0.9647
average_precision	0.9915

Training Details

Training Dataset

Unnamed Dataset

Size: 1,967 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 3 characters
mean: 20.08 characters
max: 74 characters
min: 3 characters
mean: 19.86 characters
max: 62 characters
0: ~39.60%
1: ~60.40%
Samples:
sentence1 sentence2 label
whitefield bangalore whitefield chennai 0
shastri nagar narangpura ahmedabad l-5-135 block shastri nagar ahmedabad gujarat 0
plot 45 kukatpally hyderabad plot 45 kukatpally bangalore 0
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: 282 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 282 samples:
sentence1 sentence2 label
type string string int
details
min: 4 characters
mean: 19.49 characters
max: 51 characters
min: 3 characters
mean: 19.16 characters
max: 51 characters
0: ~39.72%
1: ~60.28%
Samples:
sentence1 sentence2 label
bhargavi madhavi bhargavi srinidhi 0
navya kiara navya tanvi 0
manish manisha 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
learning_rate: 2e-05
weight_decay: 0.01
warmup_steps: 73
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: 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: None
warmup_steps: 73
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
0.1463	36	0.3950	-	-
0.2927	72	0.3475	-	-
0.2967	73	-	0.3461	0.9648
0.1463	36	0.0637	-	-
0.2927	72	0.1365	-	-
0.2967	73	-	0.4392	0.9630
0.4390	108	0.4193	-	-
0.5854	144	0.2634	-	-
0.5935	146	-	0.3738	0.9602
0.7317	180	0.3739	-	-
0.8780	216	0.1583	-	-
0.8902	219	-	0.1850	0.9893
1.0244	252	0.1806	-	-
1.1707	288	0.1287	-	-
1.1870	292	-	0.4037	0.9836
1.3171	324	0.1883	-	-
1.4634	360	0.1562	-	-
1.4837	365	-	0.3427	0.9868
1.6098	396	0.0619	-	-
1.7561	432	0.0854	-	-
1.7805	438	-	0.2923	0.9887
1.9024	468	0.1240	-	-
2.0488	504	0.1076	-	-
2.0772	511	-	0.2441	0.9915
2.1951	540	0.1289	-	-
2.3415	576	0.0311	-	-
2.3740	584	-	0.2151	0.9923
2.4878	612	0.0181	-	-
2.6341	648	0.0982	-	-
2.6707	657	-	0.2268	0.9915
2.7805	684	0.0010	-	-
2.9268	720	0.0321	-	-
2.9675	730	-	0.2393	0.9915

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: 3 characters mean: 20.08 characters max: 74 characters	min: 3 characters mean: 19.86 characters max: 62 characters	0: ~39.60% 1: ~60.40%

sentence1	sentence2	label
`whitefield bangalore`	`whitefield chennai`	`0`
`shastri nagar narangpura ahmedabad`	`l-5-135 block shastri nagar ahmedabad gujarat`	`0`
`plot 45 kukatpally hyderabad`	`plot 45 kukatpally bangalore`	`0`

sentence1	sentence2	label
`bhargavi madhavi`	`bhargavi srinidhi`	`0`
`navya kiara`	`navya tanvi`	`0`
`manish`	`manisha`	`0`