# Hugging Face

- Page: https://shared.btbytes.com/p/huggingface
- Date: 2023-05-07
- Tags: ai, llm, huggingface

> Hugging Face is a company that focuses on natural language processing (NLP) and artificial intelligence (AI) research. They are best known for their open-source library called "[transformers](https://shared.btbytes.com/p/transformers)," which provides state-of-the-art general-purpose architectures for NLP tasks. These architectures include BERT, GPT, RoBERTa, T5, and others, which are pre-trained on large datasets and can be fine-tuned for specific tasks such as sentiment analysis, machine translation, question-answering, and more.

> Hugging Face's mission is to advance AI research and democratize access to powerful NLP tools for developers, researchers, and organizations. Their platform offers an ecosystem of resources, including pre-trained models, datasets, and community-contributed tools, making it easier for users to implement and experiment with cutting-edge NLP technologies.

## Getting started instructions

 <date class="wlogdate" id="wlog-2023-05-07">2023-05-07</date>From their website on creating a new account:<a href="#wlog-2023-05-07">¶</a>

Getting started with our git and git-lfs interface

If you need to create a repo from the command line (skip if you created a repo from the website)

```
$ pip install huggingface_hub
$ huggingface-cli login
$ huggingface-cli repo create repo_name --type {model, dataset, space}
```

Clone your model or dataset locally
```
Make sure you have git-lfs installed (https://git-lfs.github.com)

$ git lfs install
$ git clone https://huggingface.co/username/repo_name
```
Then add, commit and push any file you want, including larges files

```
# save files via `.save_pretrained()` or move them here
$ git add .
$ git commit -m "commit from $USER"
$ git push
```
In most cases, if you're using one of the compatible libraries, your repo will then be accessible from code, through its identifier: `username/repo_name`.

For example for a transformers model, anyone can load it with:
```
 tokenizer = AutoTokenizer.from_pretrained("username/repo_name") model = AutoModel.from_pretrained("username/repo_name")](https://huggingface.co/welcome)
```

----

-- [Deploy Evidence on Hugging Face Spaces](https://evidence.dev/blog/hugging-face-spaces); see [evidence](https://shared.btbytes.com/p/evidence).