readme: ---
  language: 
  - multilingual
  
  tags:
  - text-2-text-generation
  - m2m_100
  ---
  
  # Model Card for KeywordIdentifier  
   
  # Model Details
   
  ## Model Description
   
  More information needed
   
  - **Developed by:** Facebook
  - **Shared by [Optional]:** Suraj Patil
  - **Model type:** Text2Text Generation
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Parent Model:** [M2M100]https://huggingface.co/facebook/m2m100_418M)
  - **Resources for more information:** 
      - [M2M100 Associated Paper](https://arxiv.org/abs/2010.11125)
  
  # Uses
   
  
  ## Direct Use
  This model can be used for the task of Text2Text Generation. 
   
  ## 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
   
  More information needed 
   
  ## Training Procedure
  
   
  ### Preprocessing
   
  More information needed 
  
  
   
  ### Speeds, Sizes, Times
   
  More information needed 
  
  
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### 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:**
   
  More information needed
  ```bibtex 
  @misc{fan2020englishcentric,
        title={Beyond English-Centric Multilingual Machine Translation}, 
        author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin},
        year={2020},
        eprint={2010.11125},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
  }
  ```
  
  
  
  **APA:**
  
  More information needed
    
  # Glossary [optional]
   
  More information needed
  
  # More Information [optional]
  See the [model hub](https://huggingface.co/models?filter=m2m_100) for more fine-tuned versions.
  
  # Model Card Authors [optional]
   
  Suraj Patil  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 AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("valhalla/m2m100_tiny_random")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("valhalla/m2m100_tiny_random")
   
   ```
  </details>
  