| ## BLEURT | |
| Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by | |
| Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research. | |
| The code for model conversion was originated from [this notebook](https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing) mentioned [here](https://github.com/huggingface/datasets/issues/224). | |
| ## Usage Example | |
| ```python | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("Elron/bleurt-large-512") | |
| model = AutoModelForSequenceClassification.from_pretrained("Elron/bleurt-large-512") | |
| model.eval() | |
| references = ["hello world", "hello world"] | |
| candidates = ["hi universe", "bye world"] | |
| with torch.no_grad(): | |
| scores = model(**tokenizer(references, candidates, return_tensors='pt'))[0].squeeze() | |
| print(scores) # tensor([0.9877, 0.0475]) | |
| ``` | |