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---
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](https://www.SBERT.net) model finetuned from [allenai/specter2_base](https://huggingface.co/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](https://huggingface.co/allenai/specter2_base) <!-- at revision 3447645e1def9117997203454fa4495937bfbd83 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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 = [
'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]
```
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Triplet
* Dataset: `triplet-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.606** |
| dot_accuracy | 0.395 |
| manhattan_accuracy | 0.603 |
| euclidean_accuracy | 0.615 |
| max_accuracy | 0.615 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 10,053 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 7.49 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.08 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.46 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
| <code>COM-induced secretome changes in U937 monocytes</code> | <code>Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes. </code> | <code>Monocytes. </code> |
| <code>Metamaterials</code> | <code>Sound attenuation optimization using metaporous materials tuned on exceptional points. </code> | <code>Metamaterials: A cat's eye for all directions. </code> |
| <code>Pediatric Parasitology</code> | <code>Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province. </code> | <code>[DIALOGUE ON PEDIATRIC PARASITOLOGY]. </code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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
</details>
### 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
```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
```bibtex
@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}
}
```
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