license: mit
language:
- en
tags:
- NLP
pipeline_tag: feature-extraction
Usage
from transformers import AutoTokenizer from model import ( BERTContrastiveLearning_simcse, BERTContrastiveLearning_simcse_w, BERTContrastiveLearning_samp, BERTContrastiveLearning_samp_w, )
str_list = data["string"].tolist() # Your list of strings here tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") tokenized_inputs = tokenizer( str_list, padding=True, max_length=50, truncation=True, return_tensors="pt" ) input_ids = tokenized_inputs["input_ids"] attention_mask = tokenized_inputs["attention_mask"]
model1 = BERTContrastiveLearning_simcse.load_from_checkpoint(ckpt1).eval() model2 = BERTContrastiveLearning_simcse_w.load_from_checkpoint(ckpt2).eval() model3 = BERTContrastiveLearning_samp.load_from_checkpoint(ckpt3).eval() model4 = BERTContrastiveLearning_samp_w.load_from_checkpoint(ckpt4).eval()
cls, _ = model(input_ids, attention_mask) # embeddings