metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:57566
  - loss:MultipleNegativesRankingLoss
base_model: allenai/specter2_base
widget:
  - source_sentence: Cannabis evolution
    sentences:
      - 'The cannabis conundrum. '
      - 'Dawn and decline of the holy smoke. '
      - '[Computer-assisted system for interstitial hyperthermia]. '
  - source_sentence: Lateral Ventricle AT/RT
    sentences:
      - >-
        Improved Assessment of Pathological Regurgitation in Patients with
        Prosthetic Heart Valves by Multiplane Transesophageal Echocardiography. 
      - '[Surgical anatomy of the lateral ventricles]. '
      - >-
        Lateral Ventricle Atypical Teratoid/Rhabdoid Tumor (AT/RT): Case Report
        and Review of Literature. 
  - source_sentence: Parkinsonian motor fluctuations
    sentences:
      - 'Basic mechanisms of motor fluctuations. '
      - 'Nonmotor Fluctuations in Parkinson''s Disease. '
      - >-
        Sodium conductance in calcium channels of single smooth muscle cells of
        guinea-pig taenia caeci. 
  - source_sentence: Phagocytic Assay
    sentences:
      - 'Assay for phagocytosis. '
      - 'Opsonophagocytic assay. '
      - >-
        Clinical evaluation of synthetic aperture sequential beamforming
        ultrasound in patients with liver tumors. 
  - source_sentence: Content validity assessment
    sentences:
      - 'Content validity is naught. '
      - >-
        Male requires a higher median target effect-site concentration of
        propofol for I-gel placement when combined with dexmedetomidine. 
      - >-
        Establishing content-validity of a disease-specific health-related
        quality of life instrument for patients with chronic hypersensitivity
        pneumonitis. 
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on allenai/specter2_base
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.04
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.22
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.3
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.04
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.06666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.044000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.03
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.03
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.18
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.27
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.15735897323110787
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.13194444444444445
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.13092350353731416
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.2
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.36
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.42
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.52
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.084
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.052000000000000005
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.2
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.36
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.42
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.52
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.35375176104312445
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.30138095238095236
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.31610409814616347
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.12000000000000001
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.28
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.32
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.41000000000000003
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12000000000000001
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.09333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.064
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.041
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.115
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.27
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.31
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.395
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.25555536713711613
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.21666269841269842
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.22351380084173883
            name: Cosine Map@100

SentenceTransformer based on allenai/specter2_base

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 dimensions
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: PeftModelForFeatureExtraction 
  (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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Content validity assessment',
    'Establishing content-validity of a disease-specific health-related quality of life instrument for patients with chronic hypersensitivity pneumonitis. ',
    'Content validity is naught. ',
]
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]

Evaluation

Metrics

Information Retrieval

Datasets: NanoNQ and NanoMSMARCO
Evaluated with InformationRetrievalEvaluator

Metric	NanoNQ	NanoMSMARCO
cosine_accuracy@1	0.04	0.2
cosine_accuracy@3	0.2	0.36
cosine_accuracy@5	0.22	0.42
cosine_accuracy@10	0.3	0.52
cosine_precision@1	0.04	0.2
cosine_precision@3	0.0667	0.12
cosine_precision@5	0.044	0.084
cosine_precision@10	0.03	0.052
cosine_recall@1	0.03	0.2
cosine_recall@3	0.18	0.36
cosine_recall@5	0.2	0.42
cosine_recall@10	0.27	0.52
cosine_ndcg@10	0.1574	0.3538
cosine_mrr@10	0.1319	0.3014
cosine_map@100	0.1309	0.3161

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.12
cosine_accuracy@3	0.28
cosine_accuracy@5	0.32
cosine_accuracy@10	0.41
cosine_precision@1	0.12
cosine_precision@3	0.0933
cosine_precision@5	0.064
cosine_precision@10	0.041
cosine_recall@1	0.115
cosine_recall@3	0.27
cosine_recall@5	0.31
cosine_recall@10	0.395
cosine_ndcg@10	0.2556
cosine_mrr@10	0.2167
cosine_map@100	0.2235

Training Details

Training Dataset

json

Dataset: json
Size: 57,566 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 7.4 tokens max: 27 tokens	min: 4 tokens mean: 19.98 tokens max: 78 tokens	min: 4 tokens mean: 12.3 tokens max: 46 tokens

Samples:

anchor	positive	negative
`neutron camera autofocus`	`The autofocusing system of the IMAT neutron camera.`	`Robust autofocusing in microscopy.`
`Melanophore-stimulating hormone-melatonin antagonism`	`Melanophore-stimulating hormone-melatonin antagonism in relation to colour change in Xenopus laevis.`	`Melanin-concentrating hormone, melanocortin receptors and regulation of luteinizing hormone release.`
`Healthcare Reform Criticism`	`Experts critique doctors' ideas for reforming health care.`	`Healthcare reform?`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
gradient_accumulation_steps: 8
learning_rate: 3e-05
weight_decay: 0.01
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: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 3e-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: 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: 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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	NanoNQ_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0	0	-	0.0633	0.2640	0.1636
0.0089	1	22.3889	-	-	-
0.0178	2	22.1875	-	-	-
0.0267	3	21.4657	-	-	-
0.0356	4	21.7306	-	-	-
0.0444	5	21.3965	-	-	-
0.0533	6	21.5539	-	-	-
0.0622	7	21.5853	-	-	-
0.0711	8	21.6282	-	-	-
0.08	9	21.2169	-	-	-
0.0889	10	21.1228	-	-	-
0.0978	11	20.7026	-	-	-
0.1067	12	21.2562	-	-	-
0.1156	13	21.1227	-	-	-
0.1244	14	20.6465	-	-	-
0.1333	15	20.5888	-	-	-
0.1422	16	20.2334	-	-	-
0.1511	17	20.6545	-	-	-
0.16	18	20.2517	-	-	-
0.1689	19	19.6825	-	-	-
0.1778	20	19.9251	-	-	-
0.1867	21	19.6937	-	-	-
0.1956	22	19.2779	-	-	-
0.2044	23	19.2927	-	-	-
0.2133	24	19.2895	-	-	-
0.2222	25	18.9854	0.1085	0.2978	0.2032
0.2311	26	18.5096	-	-	-
0.24	27	18.3789	-	-	-
0.2489	28	18.2159	-	-	-
0.2578	29	17.8306	-	-	-
0.2667	30	17.5964	-	-	-
0.2756	31	17.2527	-	-	-
0.2844	32	17.2274	-	-	-
0.2933	33	17.557	-	-	-
0.3022	34	17.4682	-	-	-
0.3111	35	16.9115	-	-	-
0.32	36	16.9938	-	-	-
0.3289	37	16.1648	-	-	-
0.3378	38	16.2908	-	-	-
0.3467	39	16.7883	-	-	-
0.3556	40	16.5278	-	-	-
0.3644	41	15.4466	-	-	-
0.3733	42	15.3954	-	-	-
0.3822	43	16.1363	-	-	-
0.3911	44	14.8857	-	-	-
0.4	45	15.5596	-	-	-
0.4089	46	15.6978	-	-	-
0.4178	47	14.6959	-	-	-
0.4267	48	15.0677	-	-	-
0.4356	49	14.4375	-	-	-
0.4444	50	15.0901	0.1348	0.3290	0.2319
0.4533	51	13.813	-	-	-
0.4622	52	14.3135	-	-	-
0.4711	53	14.9517	-	-	-
0.48	54	14.0599	-	-	-
0.4889	55	13.8699	-	-	-
0.4978	56	14.6277	-	-	-
0.5067	57	13.3742	-	-	-
0.5156	58	13.7985	-	-	-
0.5244	59	13.2972	-	-	-
0.5333	60	12.9836	-	-	-
0.5422	61	13.2035	-	-	-
0.5511	62	13.399	-	-	-
0.56	63	12.8694	-	-	-
0.5689	64	12.9775	-	-	-
0.5778	65	13.5685	-	-	-
0.5867	66	12.5359	-	-	-
0.5956	67	12.7989	-	-	-
0.6044	68	12.2337	-	-	-
0.6133	69	12.9103	-	-	-
0.6222	70	12.6319	-	-	-
0.6311	71	12.3662	-	-	-
0.64	72	12.4788	-	-	-
0.6489	73	12.7665	-	-	-
0.6578	74	12.7189	-	-	-
0.6667	75	11.6918	0.1558	0.3619	0.2588
0.6756	76	12.0761	-	-	-
0.6844	77	12.0588	-	-	-
0.6933	78	12.1507	-	-	-
0.7022	79	11.7982	-	-	-
0.7111	80	12.6278	-	-	-
0.72	81	12.1629	-	-	-
0.7289	82	11.9421	-	-	-
0.7378	83	12.1184	-	-	-
0.7467	84	11.9142	-	-	-
0.7556	85	12.1162	-	-	-
0.7644	86	12.2741	-	-	-
0.7733	87	11.8835	-	-	-
0.7822	88	11.8583	-	-	-
0.7911	89	11.74	-	-	-
0.8	90	12.0793	-	-	-
0.8089	91	11.6838	-	-	-
0.8178	92	11.6922	-	-	-
0.8267	93	11.9418	-	-	-
0.8356	94	12.2899	-	-	-
0.8444	95	12.0957	-	-	-
0.8533	96	12.0643	-	-	-
0.8622	97	12.3496	-	-	-
0.8711	98	12.3521	-	-	-
0.88	99	11.7082	-	-	-
0.8889	100	11.6085	0.1574	0.3538	0.2556
0.8978	101	11.7018	-	-	-
0.9067	102	11.8227	-	-	-
0.9156	103	12.5774	-	-	-
0.9244	104	11.465	-	-	-
0.9333	105	11.303	-	-	-
0.9422	106	11.8521	-	-	-
0.9511	107	11.6083	-	-	-
0.96	108	12.3972	-	-	-
0.9689	109	11.6962	-	-	-
0.9778	110	11.1335	-	-	-
0.9867	111	12.1325	-	-	-
0.9956	112	11.7444	-	-	-

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.3.1
Transformers: 4.49.0
PyTorch: 2.5.1
Accelerate: 1.2.1
Datasets: 2.19.0
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}