| \n## 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-base-128") |
| model = AutoModelForSequenceClassification.from_pretrained("Elron/bleurt-base-128") |
| 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([0.3598, 0.0723]) |
| ``` |
|
|
