Create README.md

README.md

@@ -0,0 +1,174 @@
+---
+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}
+}
+```