
# 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:**
     - [GitHub Repo](https://github.com/google-research/bleurt/tree/master)
 	  - [Associated Paper](https://aclanthology.org/2020.acl-main.704/)
    - [Blog Post](https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.html)
 	


# 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://aclanthology.org/2020.acl-main.704.pdf): 
> 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **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:**
 
 
```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.
 
<details>
<summary> Click to expand </summary>

```python
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](https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing) for model conversion code. 
</details>

{"tags": ["text-classification", "bert"]}
transformers pytorch bert text-classification arxiv:1910.09700 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 


# 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:
     - GitHub Repo
 	  - Associated Paper
    - Blog Post
 	


# 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.
 
BibTeX:
 
 

 
 
 
 
# 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.
 
<details>
<summary> Click to expand </summary>



See this notebook for model conversion code. 
</details>

# 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:
     - GitHub Repo
 	  - Associated Paper
    - Blog Post # 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.
 
BibTeX: # 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.
 
<details>
<summary> Click to expand </summary>



See this notebook for model conversion code. 
</details>
TAGS
#transformers #pytorch #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 
 # 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:
     - GitHub Repo
 	  - Associated Paper
    - Blog Post # 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.
 
BibTeX: # 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.
 
<details>
<summary> Click to expand </summary>



See this notebook for model conversion code. 
</details>
passage: TAGS
#transformers #pytorch #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 
# 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:
     - GitHub Repo
 	  - Associated Paper
    - Blog Post# 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