bcwarner
/

biogpt-sts-combined

@@ -1,127 +1,38 @@
----
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
----
-# {MODEL_NAME}
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-<!--- Describe your model here -->
-## Usage (Sentence-Transformers)
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-```
-pip install -U sentence-transformers
-```
-Then you can use the model like this:
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-## Evaluation Results
-<!--- Describe how your model was evaluated -->
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-## Training
-The model was trained with the parameters:
-**DataLoader**:
-`torch.utils.data.dataloader.DataLoader` of length 1641 with parameters:
-```
-{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-**Loss**:
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 10,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 100,
-    "weight_decay": 0.01
-}
-```
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BioGptModel
-  (1): Pooling({'word_embedding_dimension': 1024, '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})
-)
-```
-## Citing & Authors
-<!--- Describe where people can find more information -->

+---
+license: mit
+pipeline_tag: sentence-similarity
+tags:
+- sentence-similarity
+- sentence-transformers
+- medical
+model_name: biogpt-sts-combined
+---
+# biogpt-sts-combined
+This repo contains a fine-tuned version of microsoft/biogpt to generate semantic textual similarity pairs, primarily for use in the `sts-select` feature selection package detailed [here](https://github.com/bcwarner/sts-select).
+Details about the model and vocabulary can be in the paper [here](https://huggingface.co/papers/2308.09892).
+## Citation
+If you use this model for STS-based feature selection, please cite the following paper:
+```
+@misc{warner2023utilizing,
+      title={Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection},
+      author={Benjamin C. Warner and Ziqi Xu and Simon Haroutounian and Thomas Kannampallil and Chenyang Lu},
+      year={2023},
+      eprint={2308.09892},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+Additionally, the original model and fine-tuning papers should be cited as follows:
+```
+@article{Gu_Tinn_Cheng_Lucas_Usuyama_Liu_Naumann_Gao_Poon_2021, title={Domain-specific language model pretraining for biomedical natural language processing}, volume={3}, number={1}, journal={ACM Transactions on Computing for Healthcare (HEALTH)}, publisher={ACM New York, NY}, author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung}, year={2021}, pages={1–23} }
+@inproceedings{Cer_Diab_Agirre_Lopez-Gazpio_Specia_2017, address={Vancouver, Canada}, title={SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation}, url={https://aclanthology.org/S17-2001}, DOI={10.18653/v1/S17-2001}, booktitle={Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)}, publisher={Association for Computational Linguistics}, author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Iñigo and Specia, Lucia}, year={2017}, month=aug, pages={1–14} }
+@article{Chiu_Pyysalo_Vulić_Korhonen_2018, title={Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine}, volume={19}, number={1}, journal={BMC bioinformatics}, publisher={BioMed Central}, author={Chiu, Billy and Pyysalo, Sampo and Vulić, Ivan and Korhonen, Anna}, year={2018}, pages={1–13} }
+@inproceedings{May_2021, title={Machine translated multilingual STS benchmark dataset.}, url={https://github.com/PhilipMay/stsb-multi-mt}, author={May, Philip}, year={2021} }
+@article{Pedersen_Pakhomov_Patwardhan_Chute_2007, title={Measures of semantic similarity and relatedness in the biomedical domain}, volume={40}, number={3}, journal={Journal of biomedical informatics}, publisher={Elsevier}, author={Pedersen, Ted and Pakhomov, Serguei VS and Patwardhan, Siddharth and Chute, Christopher G}, year={2007}, pages={288–299} }
+```