valhalla/m2m100_tiny_random

1---2language: 3- multilingual4 5tags:6- text-2-text-generation7- m2m_1008---9 10# Model Card for KeywordIdentifier  11 12# Model Details13 14## Model Description15 16More information needed17 18- **Developed by:** Facebook19- **Shared by [Optional]:** Suraj Patil20- **Model type:** Text2Text Generation21- **Language(s) (NLP):** More information needed22- **License:** More information needed23- **Parent Model:** [M2M100]https://huggingface.co/facebook/m2m100_418M)24- **Resources for more information:** 25    - [M2M100 Associated Paper](https://arxiv.org/abs/2010.11125)26 27# Uses28 29 30## Direct Use31This model can be used for the task of Text2Text Generation. 32 33## Downstream Use [Optional]34 35More information needed.36 37## Out-of-Scope Use38 39The model should not be used to intentionally create hostile or alienating environments for people. 40 41# Bias, Risks, and Limitations42 43 44Significant 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.45 46 47 48## Recommendations49 50 51Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.52 53# Training Details54 55## Training Data56 57More information needed 58 59## Training Procedure60 61 62### Preprocessing63 64More information needed 65 66 67 68### Speeds, Sizes, Times69 70More information needed 71 72 73 74# Evaluation75 76 77## Testing Data, Factors & Metrics78 79### Testing Data80 81More information needed82 83### Factors84More information needed85 86### Metrics87 88More information needed89 90 91## Results 92 93More information needed94 95 96# Model Examination97 98More information needed99 100# Environmental Impact101 102Carbon 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).103 104- **Hardware Type:** More information needed105- **Hours used:** More information needed106- **Cloud Provider:** More information needed107- **Compute Region:** More information needed108- **Carbon Emitted:** More information needed109 110# Technical Specifications [optional]111 112## Model Architecture and Objective113 114More information needed115 116## Compute Infrastructure117 118More information needed119 120### Hardware121 122 123More information needed124 125### Software126 127More information needed.128 129# Citation130 131 132**BibTeX:**133 134More information needed135```bibtex 136@misc{fan2020englishcentric,137      title={Beyond English-Centric Multilingual Machine Translation}, 138      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},139      year={2020},140      eprint={2010.11125},141      archivePrefix={arXiv},142      primaryClass={cs.CL}143}144```145 146 147 148**APA:**149 150More information needed151  152# Glossary [optional]153 154More information needed155 156# More Information [optional]157See the [model hub](https://huggingface.co/models?filter=m2m_100) for more fine-tuned versions.158 159# Model Card Authors [optional]160 161Suraj Patil  in collaboration with Ezi Ozoani and the Hugging Face team162 163# Model Card Contact164 165More information needed166 167# How to Get Started with the Model168 169Use the code below to get started with the model.170 171<details>172<summary> Click to expand </summary>173 174```python175from transformers import AutoTokenizer, AutoModelForSeq2SeqLM176 177tokenizer = AutoTokenizer.from_pretrained("valhalla/m2m100_tiny_random")178 179model = AutoModelForSeq2SeqLM.from_pretrained("valhalla/m2m100_tiny_random")180 181 ```182</details>183