This is a sentence-transformers model finetuned from allenai/specter2_base on the json dataset. It maps sentences & paragraphs to a 768-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: allenai/specter2_base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- json

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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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 = [
    'Kawasaki disease immunoprophylaxis',
    '[Effect of immunoglobulin in the prevention of coronary artery aneurysms in Kawasaki disease]. ',
    'Management of Kawasaki disease. ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Training Details

Training Dataset

json

Dataset: json
Size: 8,705 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 4 tokens
mean: 7.6 tokens
max: 18 tokens
min: 6 tokens
mean: 19.26 tokens
max: 42 tokens
min: 4 tokens
mean: 11.72 tokens
max: 46 tokens

Samples:

anchor	positive	negative
`Telehealth challenges`	`[Technological transformations and evolution of the medical practice: current status, issues and perspectives for the development of telemedicine].`	`The untapped potential of Telehealth.`
`Racial disparities in mental health treatment`	`Relationships between stigma, depression, and treatment in white and African American primary care patients.`	`Mental Health Care Disparities Now and in the Future.`
`Iatrogenic hyperkalemia in elderly patients with cardiovascular disease`	`Iatrogenic hyperkalemia as a serious problem in therapy of cardiovascular diseases in elderly patients.`	`The cardiovascular implications of hypokalemia.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 2e-05
num_train_epochs: 1
lr_scheduler_type: cosine_with_restarts
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 1
max_steps: -1
lr_scheduler_type: cosine_with_restarts
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: 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: 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}
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: 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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: 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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.0110	1	2.9861
0.0220	2	2.9379
0.0330	3	3.0613
0.0440	4	2.8081
0.0549	5	2.6516
0.0659	6	2.3688
0.0769	7	2.0502
0.0879	8	1.7557
0.0989	9	1.5316
0.1099	10	1.2476
0.1209	11	1.1529
0.1319	12	0.9483
0.1429	13	0.7187
0.1538	14	0.6824
0.1648	15	0.593
0.1758	16	0.4593
0.1868	17	0.3737
0.1978	18	0.5082
0.2088	19	0.4232
0.2198	20	0.3089
0.2308	21	0.2057
0.2418	22	0.2358
0.2527	23	0.2291
0.2637	24	0.2707
0.2747	25	0.1359
0.2857	26	0.2294
0.2967	27	0.157
0.3077	28	0.0678
0.3187	29	0.1022
0.3297	30	0.0713
0.3407	31	0.0899
0.3516	32	0.1385
0.3626	33	0.0809
0.3736	34	0.1053
0.3846	35	0.0925
0.3956	36	0.0675
0.4066	37	0.0841
0.4176	38	0.0366
0.4286	39	0.0768
0.4396	40	0.0529
0.4505	41	0.0516
0.4615	42	0.0342
0.4725	43	0.0456
0.4835	44	0.0344
0.4945	45	0.1337
0.5055	46	0.0883
0.5165	47	0.0691
0.5275	48	0.0322
0.5385	49	0.0731
0.5495	50	0.0376
0.5604	51	0.0464
0.5714	52	0.0173
0.5824	53	0.0516
0.5934	54	0.0703
0.6044	55	0.0273
0.6154	56	0.0374
0.6264	57	0.0292
0.6374	58	0.1195
0.6484	59	0.0852
0.6593	60	0.0697
0.6703	61	0.0653
0.6813	62	0.0426
0.6923	63	0.0288
0.7033	64	0.0344
0.7143	65	0.104
0.7253	66	0.0251
0.7363	67	0.0095
0.7473	68	0.0208
0.7582	69	0.0814
0.7692	70	0.0813
0.7802	71	0.0508
0.7912	72	0.032
0.8022	73	0.0879
0.8132	74	0.095
0.8242	75	0.0932
0.8352	76	0.0868
0.8462	77	0.0231
0.8571	78	0.0144
0.8681	79	0.0179
0.8791	80	0.0457
0.8901	81	0.0935
0.9011	82	0.0658
0.9121	83	0.0553
0.9231	84	0.003
0.9341	85	0.0036
0.9451	86	0.0034
0.9560	87	0.0032
0.9670	88	0.0026
0.9780	89	0.0042
0.9890	90	0.0024
1.0	91	0.0022

Framework Versions

Python: 3.9.19
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.5.0
Accelerate: 1.0.1
Datasets: 2.19.0
Tokenizers: 0.20.3

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",
}

MultipleNegativesRankingLoss