Pubmedbert Mnli Snli Scinli Scitail Mednli Stsb

🚀 pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb

This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It can be used for tasks such as clustering or semantic search. The model has been trained on the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI, and STSB datasets to provide robust sentence embeddings.

🚀 Quick Start

This model is a sentence-transformers model. It maps sentences and paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search. It has been trained on the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI, and STSB datasets to provide robust sentence embeddings.

✨ Features

• Maps sentences and paragraphs to a 768-dimensional dense vector space.
• Suitable for tasks like clustering or semantic search.
• Trained on multiple datasets (SNLI, MNLI, SCINLI, SCITAIL, MEDNLI, STSB) to provide robust sentence embeddings.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without sentence-transformers, 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.

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('pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb')
model = AutoModel.from_pretrained('pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb')

# 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)

📚 Documentation

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 90 with parameters:

{'batch_size': 64, '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": 4,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 36,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 100, '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})
)

Citing & Authors

If you use the model kindly cite the following work

@inproceedings{deka2022evidence,
  title={Evidence Extraction to Validate Medical Claims in Fake News Detection},
  author={Deka, Pritam and Jurek-Loughrey, Anna and others},
  booktitle={International Conference on Health Information Science},
  pages={3--15},
  year={2022},
  organization={Springer}
}