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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:10053
	- loss:MultipleNegativesRankingLoss
	base_model: answerdotai/ModernBERT-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
	model-index:
	- name: SentenceTransformer based on answerdotai/ModernBERT-base
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: triplet dev
	type: triplet-dev
	metrics:
	- type: cosine_accuracy
	value: 0.887
	name: Cosine Accuracy
	---

	# SentenceTransformer based on answerdotai/ModernBERT-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-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: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 1e8d43065c90b6370237c1474ba1445048b02898 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- 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: ModernBertModel
	(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>
	-->

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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

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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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 \| 0.887 \|

	<!--
	## 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.
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	## 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: 8.86 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 21.84 tokens</li><li>max: 62 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 13.65 tokens</li><li>max: 50 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`: 64
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 0.0002
	- `num_train_epochs`: 2
	- `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`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 0.0002
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 2
	- `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

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| triplet-dev_cosine_accuracy \|
	\|:------:\|:----:\|:-------------:\|:---------------------------:\|
	\| 0 \| 0 \| - \| 0.457 \|
	\| 0.0189 \| 1 \| 5.2934 \| - \|
	\| 0.0377 \| 2 \| 5.2413 \| - \|
	\| 0.0566 \| 3 \| 4.9969 \| - \|
	\| 0.0755 \| 4 \| 4.5579 \| - \|
	\| 0.0943 \| 5 \| 3.9145 \| - \|
	\| 0.1132 \| 6 \| 3.3775 \| - \|
	\| 0.1321 \| 7 \| 2.8787 \| - \|
	\| 0.1509 \| 8 \| 3.0147 \| - \|
	\| 0.1698 \| 9 \| 2.7166 \| - \|
	\| 0.1887 \| 10 \| 2.7875 \| - \|
	\| 0.2075 \| 11 \| 2.3848 \| - \|
	\| 0.2264 \| 12 \| 2.1921 \| - \|
	\| 0.2453 \| 13 \| 1.7009 \| - \|
	\| 0.2642 \| 14 \| 1.7649 \| - \|
	\| 0.2830 \| 15 \| 1.7948 \| - \|
	\| 0.3019 \| 16 \| 1.5384 \| - \|
	\| 0.3208 \| 17 \| 1.6039 \| - \|
	\| 0.3396 \| 18 \| 1.3364 \| - \|
	\| 0.3585 \| 19 \| 1.3852 \| - \|
	\| 0.3774 \| 20 \| 1.2427 \| - \|
	\| 0.3962 \| 21 \| 1.3216 \| - \|
	\| 0.4151 \| 22 \| 1.4202 \| - \|
	\| 0.4340 \| 23 \| 1.2754 \| - \|
	\| 0.4528 \| 24 \| 1.281 \| - \|
	\| 0.4717 \| 25 \| 1.1709 \| 0.815 \|
	\| 0.4906 \| 26 \| 1.2363 \| - \|
	\| 0.5094 \| 27 \| 1.2169 \| - \|
	\| 0.5283 \| 28 \| 1.1495 \| - \|
	\| 0.5472 \| 29 \| 1.0066 \| - \|
	\| 0.5660 \| 30 \| 1.0478 \| - \|
	\| 0.5849 \| 31 \| 1.1511 \| - \|
	\| 0.6038 \| 32 \| 0.9992 \| - \|
	\| 0.6226 \| 33 \| 1.095 \| - \|
	\| 0.6415 \| 34 \| 1.1699 \| - \|
	\| 0.6604 \| 35 \| 0.9866 \| - \|
	\| 0.6792 \| 36 \| 1.1303 \| - \|
	\| 0.6981 \| 37 \| 1.1126 \| - \|
	\| 0.7170 \| 38 \| 0.889 \| - \|
	\| 0.7358 \| 39 \| 1.0355 \| - \|
	\| 0.7547 \| 40 \| 1.0129 \| - \|
	\| 0.7736 \| 41 \| 1.118 \| - \|
	\| 0.7925 \| 42 \| 0.8494 \| - \|
	\| 0.8113 \| 43 \| 1.0829 \| - \|
	\| 0.8302 \| 44 \| 0.8751 \| - \|
	\| 0.8491 \| 45 \| 0.8115 \| - \|
	\| 0.8679 \| 46 \| 0.8579 \| - \|
	\| 0.8868 \| 47 \| 1.1111 \| - \|
	\| 0.9057 \| 48 \| 0.9032 \| - \|
	\| 0.9245 \| 49 \| 1.0394 \| - \|
	\| 0.9434 \| 50 \| 0.9691 \| 0.862 \|
	\| 0.9623 \| 51 \| 1.023 \| - \|
	\| 0.9811 \| 52 \| 0.9465 \| - \|
	\| 1.0 \| 53 \| 0.6713 \| - \|
	\| 1.0189 \| 54 \| 0.9773 \| - \|
	\| 1.0377 \| 55 \| 0.8693 \| - \|
	\| 1.0566 \| 56 \| 0.7187 \| - \|
	\| 1.0755 \| 57 \| 0.805 \| - \|
	\| 1.0943 \| 58 \| 0.728 \| - \|
	\| 1.1132 \| 59 \| 1.0967 \| - \|
	\| 1.1321 \| 60 \| 0.7036 \| - \|
	\| 1.1509 \| 61 \| 0.8213 \| - \|
	\| 1.1698 \| 62 \| 0.57 \| - \|
	\| 1.1887 \| 63 \| 0.7006 \| - \|
	\| 1.2075 \| 64 \| 0.5091 \| - \|
	\| 1.2264 \| 65 \| 0.5758 \| - \|
	\| 1.2453 \| 66 \| 0.4484 \| - \|
	\| 1.2642 \| 67 \| 0.397 \| - \|
	\| 1.2830 \| 68 \| 0.6172 \| - \|
	\| 1.3019 \| 69 \| 0.513 \| - \|
	\| 1.3208 \| 70 \| 0.4447 \| - \|
	\| 1.3396 \| 71 \| 0.3205 \| - \|
	\| 1.3585 \| 72 \| 0.5881 \| - \|
	\| 1.3774 \| 73 \| 0.2543 \| - \|
	\| 1.3962 \| 74 \| 0.3648 \| - \|
	\| 1.4151 \| 75 \| 0.4849 \| 0.876 \|
	\| 1.4340 \| 76 \| 0.3455 \| - \|
	\| 1.4528 \| 77 \| 0.3424 \| - \|
	\| 1.4717 \| 78 \| 0.224 \| - \|
	\| 1.4906 \| 79 \| 0.18 \| - \|
	\| 1.5094 \| 80 \| 0.2255 \| - \|
	\| 1.5283 \| 81 \| 0.3024 \| - \|
	\| 1.5472 \| 82 \| 0.1835 \| - \|
	\| 1.5660 \| 83 \| 0.1946 \| - \|
	\| 1.5849 \| 84 \| 0.1958 \| - \|
	\| 1.6038 \| 85 \| 0.1568 \| - \|
	\| 1.6226 \| 86 \| 0.1626 \| - \|
	\| 1.6415 \| 87 \| 0.1774 \| - \|
	\| 1.6604 \| 88 \| 0.1934 \| - \|
	\| 1.6792 \| 89 \| 0.2426 \| - \|
	\| 1.6981 \| 90 \| 0.2958 \| - \|
	\| 1.7170 \| 91 \| 0.1606 \| - \|
	\| 1.7358 \| 92 \| 0.2281 \| - \|
	\| 1.7547 \| 93 \| 0.1786 \| - \|
	\| 1.7736 \| 94 \| 0.2241 \| - \|
	\| 1.7925 \| 95 \| 0.1909 \| - \|
	\| 1.8113 \| 96 \| 0.236 \| - \|
	\| 1.8302 \| 97 \| 0.1332 \| - \|
	\| 1.8491 \| 98 \| 0.1247 \| - \|
	\| 1.8679 \| 99 \| 0.156 \| - \|
	\| 1.8868 \| 100 \| 0.2152 \| 0.889 \|
	\| 1.9057 \| 101 \| 0.1549 \| - \|
	\| 1.9245 \| 102 \| 0.2226 \| - \|
	\| 1.9434 \| 103 \| 0.21 \| - \|
	\| 1.9623 \| 104 \| 0.2139 \| - \|
	\| 1.9811 \| 105 \| 0.1864 \| - \|
	\| 2.0 \| 106 \| 0.0719 \| 0.887 \|

	</details>

	### Framework Versions
	- Python: 3.12.3
	- Sentence Transformers: 3.3.1
	- Transformers: 4.48.0.dev0
	- PyTorch: 2.5.1
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## 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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