This is a sentence-transformers model finetuned from michiyasunaga/BioLinkBERT-large. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: michiyasunaga/BioLinkBERT-large
Maximum Sequence Length: 128 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'coactivator-associated arginine methyltransferase 1',
    'tRNA methyltransferase 13 homolog',
    'small nucleolar RNA SNORA17',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Dataset: val-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7654
spearman_cosine	0.7408

Training Details

Training Dataset

Unnamed Dataset

Size: 180,794 training samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string int
details
min: 3 tokens
mean: 8.47 tokens
max: 16 tokens
min: 3 tokens
mean: 7.63 tokens
max: 21 tokens
0: ~52.50%
1: ~47.50%
Samples:
text1 text2 label
ENSRNOG00000007053 mediator complex subunit 7 1
ENSRNOG00000060932 small nucleolar RNA SNORA55 1
ENSRNOG00000015213 ENSRNOG00000024039 0
Loss: ContrastiveLoss with these parameters:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 1.0, 4 "size_average": true 5}

Evaluation Dataset

Unnamed Dataset

Size: 22,599 evaluation samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string int
details
min: 3 tokens
mean: 8.47 tokens
max: 27 tokens
min: 3 tokens
mean: 7.73 tokens
max: 22 tokens
0: ~54.50%
1: ~45.50%
Samples:
text1 text2 label
ENSRNOG00000001350 Naa25 1
ENSRNOG00000019570 Gng3 1
AABR07040892.1 ENSRNOG00000039203 1
Loss: ContrastiveLoss with these parameters:
json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 1.0, 4 "size_average": true 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 3e-05
num_train_epochs: 100
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
ddp_find_unused_parameters: False

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 3e-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: 100
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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
use_ipex: False
bf16: False
fp16: True
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: 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}
tp_size: 0
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}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: False
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
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: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	val-eval_spearman_cosine
0.7072	500	0.1291	-	-
0.9986	706	-	0.1366	-0.0396
1.4144	1000	0.1134	-	-
1.9972	1412	-	0.1040	0.3382
2.1216	1500	0.1066	-	-
2.8289	2000	0.0943	-	-
2.9958	2118	-	0.0867	0.5349
3.5361	2500	0.0863	-	-
3.9943	2824	-	0.0825	0.5669
4.2433	3000	0.0827	-	-
4.9505	3500	0.0806	-	-
4.9929	3530	-	0.0810	0.5764
5.6577	4000	0.0782	-	-
5.9915	4236	-	0.0785	0.5923
6.3649	4500	0.0774	-	-
6.9901	4942	-	0.0774	0.6017
7.0721	5000	0.0758	-	-
7.7793	5500	0.0735	-	-
7.9887	5648	-	0.0773	0.6034
8.4866	6000	0.0719	-	-
8.9873	6354	-	0.0765	0.6052
9.1938	6500	0.0701	-	-
9.9010	7000	0.0685	-	-
9.9859	7060	-	0.0753	0.6165
10.6082	7500	0.0651	-	-
10.9844	7766	-	0.0742	0.6215
11.3154	8000	0.0634	-	-
11.9830	8472	-	0.0730	0.6345
12.0226	8500	0.0612	-	-
12.7298	9000	0.0567	-	-
12.9816	9178	-	0.0720	0.6401
13.4371	9500	0.0538	-	-
13.9802	9884	-	0.0708	0.6514
14.1443	10000	0.0517	-	-
14.8515	10500	0.048	-	-
14.9788	10590	-	0.0691	0.6616
15.5587	11000	0.0436	-	-
15.9774	11296	-	0.0681	0.6692
16.2659	11500	0.0417	-	-
16.9731	12000	0.0394	-	-
16.9760	12002	-	0.0659	0.6819
17.6803	12500	0.0345	-	-
17.9745	12708	-	0.0636	0.6954
18.3876	13000	0.033	-	-
18.9731	13414	-	0.0621	0.7027
19.0948	13500	0.0313	-	-
19.8020	14000	0.028	-	-
19.9717	14120	-	0.0615	0.7066
20.5092	14500	0.0258	-	-
20.9703	14826	-	0.0598	0.7144
21.2164	15000	0.0249	-	-
21.9236	15500	0.0231	-	-
21.9689	15532	-	0.0587	0.7191
22.6308	16000	0.0207	-	-
22.9675	16238	-	0.0582	0.7215
23.3380	16500	0.0199	-	-
23.9661	16944	-	0.0575	0.7245
24.0453	17000	0.0194	-	-
24.7525	17500	0.0169	-	-
24.9646	17650	-	0.0562	0.7293
25.4597	18000	0.0161	-	-
25.9632	18356	-	0.0557	0.7327
26.1669	18500	0.0159	-	-
26.8741	19000	0.0146	-	-
26.9618	19062	-	0.0550	0.7342
27.5813	19500	0.0134	-	-
27.9604	19768	-	0.0551	0.7340
28.2885	20000	0.0132	-	-
28.9590	20474	-	0.0544	0.7373
28.9958	20500	0.0127	-	-
29.7030	21000	0.0112	-	-
29.9576	21180	-	0.0538	0.7387
30.4102	21500	0.011	-	-
30.9562	21886	-	0.0534	0.7403
31.1174	22000	0.0109	-	-
31.8246	22500	0.0099	-	-
31.9547	22592	-	0.0536	0.7402
32.5318	23000	0.0094	-	-
32.9533	23298	-	0.0530	0.7421
33.2390	23500	0.0093	-	-
33.9463	24000	0.0091	-	-
33.9519	24004	-	0.0528	0.7425
34.6535	24500	0.0081	-	-
34.9505	24710	-	0.0524	0.7435
35.3607	25000	0.0081	-	-
35.9491	25416	-	0.0529	0.7421
36.0679	25500	0.008	-	-
36.7751	26000	0.0072	-	-
36.9477	26122	-	0.0526	0.7426
37.4823	26500	0.007	-	-
37.9463	26828	-	0.0522	0.7439
38.1895	27000	0.007	-	-
38.8967	27500	0.0067	-	-
38.9448	27534	-	0.0529	0.7416
39.6040	28000	0.0062	-	-
39.9434	28240	-	0.0523	0.7425
40.3112	28500	0.0062	-	-
40.9420	28946	-	0.0529	0.7408

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 3.5.1
Tokenizers: 0.21.1

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",
}

ContrastiveLoss

bibtex

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

	text1	text2	label
type	string	string	int
details	min: 3 tokens mean: 8.47 tokens max: 16 tokens	min: 3 tokens mean: 7.63 tokens max: 21 tokens	0: ~52.50% 1: ~47.50%

text1	text2	label
`ENSRNOG00000007053`	`mediator complex subunit 7`	`1`
`ENSRNOG00000060932`	`small nucleolar RNA SNORA55`	`1`
`ENSRNOG00000015213`	`ENSRNOG00000024039`	`0`

text1	text2	label
`ENSRNOG00000001350`	`Naa25`	`1`
`ENSRNOG00000019570`	`Gng3`	`1`
`AABR07040892.1`	`ENSRNOG00000039203`	`1`

mghuibregtse/biolinkbert-large-simcse-rat

SentenceTransformer based on michiyasunaga/BioLinkBERT-large

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

ContrastiveLoss

mghuibregtse/
biolinkbert-large-simcse-rat