metadata

base_model: allenai/specter2_base
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10053
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: HBV-endemic area diagnostic criteria comparison
    sentences:
      - >-
        Comparison of usefulness of clinical diagnostic criteria for
        hepatocellular carcinoma in a hepatitis B endemic area. 
      - >-
        The validation of the 2010 American Association for the Study of Liver
        Diseases guideline for the diagnosis of hepatocellular carcinoma in an
        endemic area. 
      - >-
        Which admission electrocardiographic parameter is more powerful
        predictor of no-reflow in patients with acute anterior myocardial
        infarction who underwent primary percutaneous intervention? 
  - source_sentence: Family history of alcoholism classification schemes
    sentences:
      - 'Developing the mentor/protege relationship. '
      - 'Family history of alcoholism in schizophrenia. '
      - >-
        Family history models of alcoholism: age of onset, consequences and
        dependence. 
  - source_sentence: Intellectual Property Commercialization
    sentences:
      - >-
        ALEPH-2, a suspected anxiolytic and putative hallucinogenic
        phenylisopropylamine derivative, is a 5-HT2a and 5-HT2c receptor
        agonist. 
      - 'Technology transfer and monitoring practices. '
      - '[From intellectual property to commercial property]. '
  - source_sentence: Transmembrane domain mutants
    sentences:
      - >-
        Dysgerminoma; case with pulmonary metastases; result of treatment with
        irradiation and male sex hormone. 
      - >-
        Toward a high-resolution structure of phospholamban: design of soluble
        transmembrane domain mutants. 
      - 'Scanning N-glycosylation mutagenesis of membrane proteins. '
  - source_sentence: Six-coordinate low-spin iron(III) porphyrinate complexes
    sentences:
      - >-
        Molecular structures and magnetic resonance spectroscopic investigations
        of highly distorted six-coordinate low-spin iron(III) porphyrinate
        complexes. 
      - >-
        Saddle-shaped six-coordinate iron(iii) porphyrin complex with unusual
        intermediate-spin electronic structure. 
      - >-
        Performing Economic Evaluation of Integrated Care: Highway to Hell or
        Stairway to Heaven? 
model-index:
  - name: SentenceTransformer based on allenai/specter2_base
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: triplet dev
          type: triplet-dev
        metrics:
          - type: cosine_accuracy
            value: 0.606
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.395
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.603
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.615
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.615
            name: Max Accuracy

SentenceTransformer based on allenai/specter2_base

This model is an initial proof of concept for (yet unpublished) article on ultra-hard negative triplet generation. While the original Specter2 adapters were trained on 600k triplets, only 10k ultra-hard, self-supervised negatives were enough to outperform the Proximity adapter (85 vs 84.1 avg NDCG over Relish, NFCorpus, TREC CoVID).

Model Details

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 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Six-coordinate low-spin iron(III) porphyrinate complexes',
    'Molecular structures and magnetic resonance spectroscopic investigations of highly distorted six-coordinate low-spin iron(III) porphyrinate complexes. ',
    'Saddle-shaped six-coordinate iron(iii) porphyrin complex with unusual intermediate-spin electronic structure. ',
]
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

Triplet

Dataset: triplet-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.606
dot_accuracy	0.395
manhattan_accuracy	0.603
euclidean_accuracy	0.615
max_accuracy	0.615

Training Details

Training Dataset

json

Dataset: json
Size: 10,053 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: 7.49 tokens max: 18 tokens	min: 4 tokens mean: 20.08 tokens max: 48 tokens	min: 4 tokens mean: 12.46 tokens max: 48 tokens

Samples:

anchor	positive	negative
`COM-induced secretome changes in U937 monocytes`	`Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes.`	`Monocytes.`
`Metamaterials`	`Sound attenuation optimization using metaporous materials tuned on exceptional points.`	`Metamaterials: A cat's eye for all directions.`
`Pediatric Parasitology`	`Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province.`	`[DIALOGUE ON PEDIATRIC PARASITOLOGY].`

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: 32
per_device_eval_batch_size: 32
learning_rate: 2e-05
num_train_epochs: 6
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: 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: 6
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	triplet-dev_cosine_accuracy
0	0	-	0.373
0.1667	1	3.138	-
0.3333	2	2.9761	-
0.5	3	2.7135	-
0.6667	4	2.5144	-
0.8333	5	1.9797	-
1.0	6	1.2683	-
1.1667	7	1.6058	-
1.3333	8	1.3236	-
1.5	9	1.1134	-
1.6667	10	1.1205	-
1.8333	11	0.9369	-
2.0	12	0.6215	-
2.1667	13	1.0374	-
2.3333	14	0.9355	-
2.5	15	0.7118	-
2.6667	16	0.7967	-
2.8333	17	0.5739	-
3.0	18	0.4515	-
3.1667	19	0.8018	-
3.3333	20	0.6557	-
3.5	21	0.6027	-
3.6667	22	0.6747	-
3.8333	23	0.5013	-
4.0	24	0.1428	-
4.1667	25	0.5889	0.596
4.3333	26	0.5439	-
4.5	27	0.4742	-
4.6667	28	0.5734	-
4.8333	29	0.3966	-
5.0	30	0.1793	-
5.1667	31	0.5408	-
5.3333	32	0.5174	-
5.5	33	0.4179	-
5.6667	34	0.4589	-
5.8333	35	0.3683	-
6.0	36	0.1442	0.606

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

@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}
}