readme: ---
  tags:
  - text-classification
  - bert
  ---
  
  # 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>
  