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---
license: mit
language:
- en
tags:
- NLP
pipeline_tag: feature-extraction
---
# Usage
```python
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
``` |