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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:10053
	- loss:MultipleNegativesRankingLoss
	base_model: allenai/specter2_base
	widget:
	- source_sentence: Fluorescence quenching of tryptophan residues
	sentences:
	- 'Fluorescence of buried tyrosine residues in proteins. '
	- 'A fluorescence quenching study of tryptophanyl residues of (Ca2+ + Mg2+)-ATPase
	from sarcoplasmic reticulum. '
	- 'Some hormonal influences on the acetylation of sulfanilamide in vivo. '
	- source_sentence: Human migration to the Americas
	sentences:
	- 'Homo sapiens in the Americas. Overview of the earliest human expansion in the
	New World. '
	- 'Profiles of College Drinkers Defined by Alcohol Behaviors at the Week Level:
	Replication Across Semesters and Prospective Associations With Hazardous Drinking
	and Dependence-Related Symptoms. '
	- 'Human migration. '
	- source_sentence: Human Mobility Prediction
	sentences:
	- 'Human mobility prediction from region functions with taxi trajectories. '
	- 'Understanding Human Mobility from Twitter. '
	- 'Ovarian cancer gene therapy using HPV-16 pseudovirion carrying the HSV-tk gene. '
	- source_sentence: Nevirapine Resistance
	sentences:
	- 'Nevirapine toxicity. '
	- 'Recognizing rhenium. '
	- 'Update on nevirapine: quest for a niche. '
	- source_sentence: EHL tendon reconstruction
	sentences:
	- 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two
	Case Reports. '
	- 'Flexor tendon reconstruction. '
	- 'Noble gases and neuroprotection: summary of current evidence. '
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	- dot_accuracy
	- manhattan_accuracy
	- euclidean_accuracy
	- max_accuracy
	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: 0.573
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.455
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.576
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.577
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.577
	name: Max Accuracy
	---

	# SentenceTransformer based on allenai/specter2_base

	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 Details

	### 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: 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:

	```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 = [
	'EHL tendon reconstruction',
	'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ',
	'Flexor tendon reconstruction. ',
	]
	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]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## 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 \| 0.573 \|
	\| dot_accuracy \| 0.455 \|
	\| manhattan_accuracy \| 0.576 \|
	\| euclidean_accuracy \| 0.577 \|
	\| max_accuracy \| 0.577 \|

	<!--
	## 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: 7.54 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 20.11 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 12.36 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`: 512
	- `per_device_eval_batch_size`: 512
	- `learning_rate`: 0.001
	- `num_train_epochs`: 1
	- `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`: 512
	- `per_device_eval_batch_size`: 512
	- `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`: 0.001
	- `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

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| triplet-dev_cosine_accuracy \|
	\|:-----:\|:----:\|:-------------:\|:---------------------------:\|
	\| 0 \| 0 \| - \| 0.373 \|
	\| 0.05 \| 1 \| 4.5633 \| - \|
	\| 0.1 \| 2 \| 4.5857 \| - \|
	\| 0.15 \| 3 \| 4.1852 \| - \|
	\| 0.2 \| 4 \| 3.2547 \| - \|
	\| 0.25 \| 5 \| 2.3117 \| - \|
	\| 0.3 \| 6 \| 1.949 \| - \|
	\| 0.35 \| 7 \| 1.7767 \| - \|
	\| 0.4 \| 8 \| 1.79 \| - \|
	\| 0.45 \| 9 \| 1.6081 \| - \|
	\| 0.5 \| 10 \| 1.7499 \| - \|
	\| 0.55 \| 11 \| 1.6395 \| - \|
	\| 0.6 \| 12 \| 1.5645 \| - \|
	\| 0.65 \| 13 \| 1.5804 \| - \|
	\| 0.7 \| 14 \| 1.5303 \| - \|
	\| 0.75 \| 15 \| 1.5452 \| - \|
	\| 0.8 \| 16 \| 1.5012 \| - \|
	\| 0.85 \| 17 \| 1.5283 \| - \|
	\| 0.9 \| 18 \| 1.5982 \| - \|
	\| 0.95 \| 19 \| 1.4714 \| - \|
	\| 1.0 \| 20 \| 1.3331 \| 0.573 \|


	### 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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