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("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['A Hydro Flask in a light brown color with a small hand logo.', 'A large, light-brown Hydro Flask water bottle with a darker tan cap and black accents, appears to be made of metal, and seems to be in new condition with tags still attached.'],
    ['A black smartphone.', 'The image shows four used smartphones, including a white and black Samsung smartphone, a black and silver phone of unknown brand, a white and black Nokia phone, and a white Apple iPhone, all appearing to be between 4 and 5 inches in screen size.'],
    ['A purple pencil case with a unicorn design.', 'A new, mint green hard-shell pencil case with a ribbed texture and a central circular illustration of a unicorn with a rainbow mane.'],
    ['A folded, dark blue umbrella has a slightly crinkled matching fabric case and its handle is still wrapped in clear plastic.', 'There are two blue umbrellas.'],
    ['a black messenger bag with purple stitching.', 'A gray-green backpack with black mesh padding and an orange "NANEU PRO" tag on the side.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'A Hydro Flask in a light brown color with a small hand logo.',
    [
        'A large, light-brown Hydro Flask water bottle with a darker tan cap and black accents, appears to be made of metal, and seems to be in new condition with tags still attached.',
        'The image shows four used smartphones, including a white and black Samsung smartphone, a black and silver phone of unknown brand, a white and black Nokia phone, and a white Apple iPhone, all appearing to be between 4 and 5 inches in screen size.',
        'A new, mint green hard-shell pencil case with a ribbed texture and a central circular illustration of a unicorn with a rainbow mane.',
        'There are two blue umbrellas.',
        'A gray-green backpack with black mesh padding and an orange "NANEU PRO" tag on the side.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Binary Classification

Dataset: eval
Evaluated with CEBinaryClassificationEvaluator

Metric	Value
accuracy	0.8962
accuracy_threshold	0.2969
f1	0.7976
f1_threshold	0.2016
precision	0.7505
recall	0.8511
average_precision	0.8669

Training Details

Training Dataset

Unnamed Dataset

Size: 68,056 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 18 characters
mean: 104.96 characters
max: 313 characters
min: 15 characters
mean: 116.53 characters
max: 482 characters
min: 0.0
mean: 0.23
max: 1.0

Samples:

sentence_0	sentence_1	label
`A Hydro Flask in a light brown color with a small hand logo.`	`A large, light-brown Hydro Flask water bottle with a darker tan cap and black accents, appears to be made of metal, and seems to be in new condition with tags still attached.`	`1.0`
`A black smartphone.`	`The image shows four used smartphones, including a white and black Samsung smartphone, a black and silver phone of unknown brand, a white and black Nokia phone, and a white Apple iPhone, all appearing to be between 4 and 5 inches in screen size.`	`0.0`
`A purple pencil case with a unicorn design.`	`A new, mint green hard-shell pencil case with a ribbed texture and a central circular illustration of a unicorn with a rainbow mane.`	`0.0`

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_train_batch_size: 16
per_device_eval_batch_size: 16

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: True
label_names: None
load_best_model_at_end: False
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	eval_average_precision
0.1175	500	0.3493	0.7918
0.2351	1000	0.3064	0.8216
0.3526	1500	0.2832	0.8328
0.4701	2000	0.2873	0.8408
0.5877	2500	0.2866	0.8502
0.7052	3000	0.2797	0.8499
0.8228	3500	0.2737	0.8525
0.9403	4000	0.2724	0.8563
1.0	4254	-	0.8587
1.0578	4500	0.2718	0.8565
1.1754	5000	0.264	0.8561
1.2929	5500	0.2642	0.8584
1.4104	6000	0.2604	0.8582
1.5280	6500	0.2593	0.8595
1.6455	7000	0.2498	0.8628
1.7630	7500	0.2515	0.8649
1.8806	8000	0.2504	0.8650
1.9981	8500	0.2624	0.8643
2.0	8508	-	0.8632
2.1157	9000	0.2481	0.8662
2.2332	9500	0.2483	0.8661
2.3507	10000	0.2543	0.8647
2.4683	10500	0.2473	0.8669

Framework Versions

Python: 3.12.10
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.9.1+cu128
Accelerate: 1.11.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 18 characters mean: 104.96 characters max: 313 characters	min: 15 characters mean: 116.53 characters max: 482 characters	min: 0.0 mean: 0.23 max: 1.0

ingridchien/harvard-loop-reranker

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