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model documentation (#3)
Browse files- model documentation (968a513516bbc0a9388dbc511583c5a1b80bd5e3)
Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>
README.md
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---
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language:
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- en
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---
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# Model Card for tiny-wav2vec2-no-tokenizer
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# Model Details
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## Model Description
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- **Developed by:** More information needed
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- **Shared by [Optional]:** Patrick von Platen
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- **Model type:** Automatic Speech Recognition
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- **Language(s) (NLP):** en
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- **License:** More information needed
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- **Related Models:**
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- **Parent Model:** Wav2Vec2
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- **Resources for more information:**
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- [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec#wav2vec-20)
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- [Associated Paper](https://arxiv.org/abs/2006.11477)
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- [Associated Model Doc](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2)
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# Uses
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## Direct Use
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This model can be used for the task of Automatic Speech Recognition
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## Downstream Use [Optional]
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More information needed
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## Out-of-Scope Use
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The model should not be used to intentionally create hostile or alienating environments for people.
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# Bias, Risks, and Limitations
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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.
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## Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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# Training Details
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## Training Data
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More information needed
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## Training Procedure
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### Preprocessing
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More information needed
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### Speeds, Sizes, Times
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More information needed
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# Evaluation
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## Testing Data, Factors & Metrics
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### Testing Data
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More information needed
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### Factors
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### Metrics
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More information needed
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## Results
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More information needed
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# Model Examination
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More information needed
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# Environmental Impact
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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).
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- **Hardware Type:** More information needed
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- **Hours used:** More information needed
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- **Cloud Provider:** More information needed
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- **Compute Region:** More information needed
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- **Carbon Emitted:** More information needed
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# Technical Specifications [optional]
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## Model Architecture and Objective
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More information needed
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## Compute Infrastructure
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More information needed
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### Hardware
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More information needed
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### Software
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More information needed
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# Citation
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**BibTeX:**
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```
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@misc{https://doi.org/10.48550/arxiv.2006.11477,
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doi = {10.48550/ARXIV.2006.11477},
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url = {https://arxiv.org/abs/2006.11477},
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author = {Baevski, Alexei and Zhou, Henry and Mohamed, Abdelrahman and Auli, Michael},
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keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
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title = {wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations},
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publisher = {arXiv},
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```
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# Glossary [optional]
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More information needed
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# More Information [optional]
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More information needed
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# Model Card Authors [optional]
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Patrick von Platen in collaboration with the Hugging Face team
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# Model Card Contact
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More information needed
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# How to Get Started with the Model
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Use the code below to get started with the model.
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<details>
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<summary> Click to expand </summary>
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```python
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from transformers import AutoModel
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model = AutoModel.from_pretrained("patrickvonplaten/tiny-wav2vec2-no-tokenizer")
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```
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</details>
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