README.md

---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---

# Data2Vec-Text base model

Pretrained model on English language using the *data2vec* objective. It was introduced in
[this paper](https://arxiv.org/abs/2202.03555) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/main/examples/data2vec). This model is case-sensitive: it
makes a difference between english and English.

Disclaimer: The team releasing Data2Vec-Text did not write a model card for this model so this model card has been written by
the Hugging Face team.

## Abstract

*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because
they were developed with a single modality in
mind. To get us closer to general self-supervised
learning, we present data2vec, a framework that
uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data
based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific
targets such as words, visual tokens or units of
human speech which are local in nature, data2vec
predicts contextualized latent representations that
contain information from the entire input. Experiments on the major benchmarks of speech
recognition, image classification, and natural language understanding demonstrate a new state of
the art or competitive performance to predominant approaches.*

## Intended uses & limitations

You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=data2vec-text) to look for fine-tuned versions on a task that
interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.

### How to use

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='facebook/data2vec-text-base')
>>> unmasker("Hello I'm a <mask> model.")

[{'sequence': "<s>Hello I'm a male model.</s>",
  'score': 0.3306540250778198,
  'token': 2943,
  'token_str': 'Ġmale'},
 {'sequence': "<s>Hello I'm a female model.</s>",
  'score': 0.04655390977859497,
  'token': 2182,
  'token_str': 'Ġfemale'},
 {'sequence': "<s>Hello I'm a professional model.</s>",
  'score': 0.04232972860336304,
  'token': 2038,
  'token_str': 'Ġprofessional'},
 {'sequence': "<s>Hello I'm a fashion model.</s>",
  'score': 0.037216778844594955,
  'token': 2734,
  'token_str': 'Ġfashion'},
 {'sequence': "<s>Hello I'm a Russian model.</s>",
  'score': 0.03253649175167084,
  'token': 1083,
  'token_str': 'ĠRussian'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('facebook/data2vec-text-base')
model = AutoModel.from_pretrained('facebook/data2vec-text-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

### Limitations and bias

The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='facebook/data2vec-text-base')
>>> unmasker("The man worked as a <mask>.")

[{'sequence': '<s>The man worked as a mechanic.</s>',
  'score': 0.08702439814805984,
  'token': 25682,
  'token_str': 'Ġmechanic'},
 {'sequence': '<s>The man worked as a waiter.</s>',
  'score': 0.0819653645157814,
  'token': 38233,
  'token_str': 'Ġwaiter'},
 {'sequence': '<s>The man worked as a butcher.</s>',
  'score': 0.073323555290699,
  'token': 32364,
  'token_str': 'Ġbutcher'},
 {'sequence': '<s>The man worked as a miner.</s>',
  'score': 0.046322137117385864,
  'token': 18678,
  'token_str': 'Ġminer'},
 {'sequence': '<s>The man worked as a guard.</s>',
  'score': 0.040150221437215805,
  'token': 2510,
  'token_str': 'Ġguard'}]

>>> unmasker("The Black woman worked as a <mask>.")

[{'sequence': '<s>The Black woman worked as a waitress.</s>',
  'score': 0.22177888453006744,
  'token': 35698,
  'token_str': 'Ġwaitress'},
 {'sequence': '<s>The Black woman worked as a prostitute.</s>',
  'score': 0.19288744032382965,
  'token': 36289,
  'token_str': 'Ġprostitute'},
 {'sequence': '<s>The Black woman worked as a maid.</s>',
  'score': 0.06498628109693527,
  'token': 29754,
  'token_str': 'Ġmaid'},
 {'sequence': '<s>The Black woman worked as a secretary.</s>',
  'score': 0.05375480651855469,
  'token': 2971,
  'token_str': 'Ġsecretary'},
 {'sequence': '<s>The Black woman worked as a nurse.</s>',
  'score': 0.05245552211999893,
  'token': 9008,
  'token_str': 'Ġnurse'}]
```

This bias will also affect all fine-tuned versions of this model.

## Training data

The RoBERTa model was pretrained on the reunion of five datasets:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
  articles crawled between September 2016 and February 2019.
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
  train GPT-2,
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
  story-like style of Winograd schemas.

Together theses datasets weight 160GB of text.


### BibTeX entry and citation info

```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
  doi = {10.48550/ARXIV.2202.03555},
  url = {https://arxiv.org/abs/2202.03555},
  author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```