Model Card for bleurt-tiny-512
Model Details
Model Description
Pytorch version of the original BLEURT models from ACL paper
- Developed by: Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research
- Shared by [Optional]: Elron Bandel
- Model type: Text Classification
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model: BERT
- Resources for more information:
Uses
Direct Use
This model can be used for the task of Text Classification
Downstream Use [Optional]
More information needed.
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
The model authors note in the associated paper:
We use years 2017 to 2019 of the WMT Metrics Shared Task, to-English language pairs. For each year, we used the of- ficial WMT test set, which include several thou- sand pairs of sentences with human ratings from the news domain. The training sets contain 5,360, 9,492, and 147,691 records for each year.
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
The test sets for years 2018 and 2019 [of the WMT Metrics Shared Task, to-English language pairs.] are noisier,
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed.
Citation
BibTeX:
@inproceedings{sellam2020bleurt,
title = {BLEURT: Learning Robust Metrics for Text Generation},
author = {Thibault Sellam and Dipanjan Das and Ankur P Parikh},
year = {2020},
booktitle = {Proceedings of ACL}
}
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Elron Bandel in collaboration with Ezi Ozoani and the Hugging Face team
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("Elron/bleurt-tiny-512")
model = AutoModelForSequenceClassification.from_pretrained("Elron/bleurt-tiny-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([-0.9414, -0.5678])
See this notebook for model conversion code.
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