This is a Cross Encoder model finetuned from cambridgeltl/SapBERT-from-PubMedBERT-fulltext 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: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label

Language: en

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 = [
    ['Original mention: L-N.\nContext: Partial HPRT deficiencies are associated with gouty arthritis, while absence of activity results in Lesch-Nyhan syndrome (L-N).', 'guanine phosphoribosyltransferase deficiencies'],
    ['Original mention: breast cancer.\nContext: Most multiple case families of young onset breast cancer and ovarian cancer are thought to be due to highly penetrant mutations in the predisposing genes BRCA1 and BRCA2.', 'malignancies'],
    ['Original mention: AS.\nContext: We report here the characterization of a transgene insertion (Epstein-Barr virus Latent Membrane Protein 2A, LMP2A) into mouse chromosome 7C, which has resulted in mouse models for PWS and AS dependent on the sex of the transmitting parent.', 'achondroplastic dwarfism'],
    ['Original mention: adenomatous polyposis coli.\nContext: Epidemiologic studies have shown an increased frequency of this tumor type in families affected by adenomatous polyposis coli.', 'apc (adenomatous polyposis coli)'],
    ['Original mention: autosomal recessive disorder.\nContext: Mucopolysaccharidosis IVA (MPS IVA) is an autosomal recessive disorder caused by a deficiency in N-acetylgalactosamine-6-sulfatase (GALNS).', 'susceptibility, disease'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Original mention: L-N.\nContext: Partial HPRT deficiencies are associated with gouty arthritis, while absence of activity results in Lesch-Nyhan syndrome (L-N).',
    [
        'guanine phosphoribosyltransferase deficiencies',
        'malignancies',
        'achondroplastic dwarfism',
        'apc (adenomatous polyposis coli)',
        'susceptibility, disease',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Dataset: ncbi-disease-dev
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": false 4}

Metric	Value
map	0.9979 (+0.5536)
mrr@10	0.9986 (+0.7236)
ndcg@10	0.9987 (+0.4194)

Training Details

Training Dataset

Unnamed Dataset

Size: 222,348 training samples
Columns: query, answer, and label
Approximate statistics based on the first 1000 samples:
query answer label
type string string int
details
min: 64 characters
mean: 207.06 characters
max: 558 characters
min: 5 characters
mean: 27.99 characters
max: 107 characters
0: ~46.10%
1: ~53.90%

Samples:

query	answer	label
`Original mention: L-N.[object Object]Context: Partial HPRT deficiencies are associated with gouty arthritis, while absence of activity results in Lesch-Nyhan syndrome (L-N).`	`guanine phosphoribosyltransferase deficiencies`	`1`
`Original mention: breast cancer.[object Object]Context: Most multiple case families of young onset breast cancer and ovarian cancer are thought to be due to highly penetrant mutations in the predisposing genes BRCA1 and BRCA2.`	`malignancies`	`0`
`Original mention: AS.[object Object]Context: We report here the characterization of a transgene insertion (Epstein-Barr virus Latent Membrane Protein 2A, LMP2A) into mouse chromosome 7C, which has resulted in mouse models for PWS and AS dependent on the sex of the transmitting parent.`	`achondroplastic dwarfism`	`0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": 0.7792357206344604 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
warmup_ratio: 0.05
seed: 12
bf16: True
dataloader_num_workers: 4
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: 128
per_device_eval_batch_size: 128
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: 0.0
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: {}
warmup_ratio: 0.05
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: 12
data_seed: None
jit_mode_eval: False
bf16: True
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: 4
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
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	ncbi-disease-dev_ndcg@10
0.0006	1	0.5977	-
0.0863	150	0.5711	-
0.1726	300	0.3596	-
0.2589	450	0.2451	-
0.3452	600	0.1933	-
0.4315	750	0.1629	-
0.5178	900	0.1448	-
0.6041	1050	0.1271	-
0.6904	1200	0.1102	-
0.7768	1350	0.0986	-
0.8631	1500	0.0927	0.9963 (+0.4169)
0.9494	1650	0.0821	-
1.0357	1800	0.073	-
1.1220	1950	0.0641	-
1.2083	2100	0.0544	-
1.2946	2250	0.055	-
1.3809	2400	0.0556	-
1.4672	2550	0.0546	-
1.5535	2700	0.0514	-
1.6398	2850	0.0463	-
1.7261	3000	0.0416	0.9984 (+0.4190)
1.8124	3150	0.043	-
1.8987	3300	0.0433	-
1.9850	3450	0.0425	-
2.0713	3600	0.0322	-
2.1577	3750	0.0272	-
2.2440	3900	0.0273	-
2.3303	4050	0.0274	-
2.4166	4200	0.0265	-
2.5029	4350	0.0285	-
2.5892	4500	0.0249	0.9987 (+0.4194)
2.6755	4650	0.0263	-
2.7618	4800	0.0252	-
2.8481	4950	0.0256	-
2.9344	5100	0.0247	-
-1	-1	-	0.9987 (+0.4194)

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu130
Accelerate: 1.12.0
Datasets: 4.4.2
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",
}

	query	answer	label
type	string	string	int
details	min: 64 characters mean: 207.06 characters max: 558 characters	min: 5 characters mean: 27.99 characters max: 107 characters	0: ~46.10% 1: ~53.90%

OverSamu/reranker-sapbert-ncbi-disease-bce-context

Cambridge-SapBERT-from-PubMedBERT-fulltext trained on NCBI Disease