
A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Pubmed sentences. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.

```python

from transformers import AutoTokenizer, AutoModel
import torch

def load_finetuned_model():


        sentence_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed", revision="71f4539120e29024adc618173a1ed5fd230ac249")
        query_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-query-pubmed", revision="8d34676d80a39bcbc5a1d2eec13e6f8078496215")
        tokenizer = AutoTokenizer.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
        return tokenizer, query_encoder, sentence_encoder


def encode_batch(model, tokenizer, sentences, device):
    input_ids = tokenizer(sentences, padding=True, max_length=128, truncation=True, return_tensors="pt",
                          add_special_tokens=True).to(device)
    features = model(**input_ids)[0]

    features =  torch.sum(features[:,:,:] * input_ids["attention_mask"][:,:].unsqueeze(-1), dim=1) / torch.clamp(torch.sum(input_ids["attention_mask"][:,:], dim=1, keepdims=True), min=1e-9)

    return features

```
{"language": ["en"], "tags": ["feature-extraction", "pubmed", "sentence-similarity"], "datasets": ["biu-nlp/abstract-sim-pubmed"]}
transformers pytorch bert fill-mask feature-extraction pubmed sentence-similarity en dataset:biu-nlp/abstract-sim-pubmed autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #feature-extraction #pubmed #sentence-similarity #en #dataset-biu-nlp/abstract-sim-pubmed #autotrain_compatible #endpoints_compatible #region-us 


A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Pubmed sentences. Use  to load the query and sentence encoder, and  to encode a sentence with the model.


TAGS
#transformers #pytorch #bert #fill-mask #feature-extraction #pubmed #sentence-similarity #en #dataset-biu-nlp/abstract-sim-pubmed #autotrain_compatible #endpoints_compatible #region-us 
