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----
-language: en
-tags:
-- exbert
-license: mit
----
-# GPT-2
-Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
-Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
-[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
-and first released at [this page](https://openai.com/blog/better-language-models/).
-Disclaimer: The team releasing GPT-2 also wrote a
-[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
-has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
-## Model description
-GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
-means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
-of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
-it was trained to guess the next word in sentences.
-More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
-shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
-predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
-This way, the model learns an inner representation of the English language that can then be used to extract features
-useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
-prompt.
-## Intended uses & limitations
-You can use the raw model for text generation or fine-tune it to a downstream task. See the
-[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
-### How to use
-You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
-set a seed for reproducibility:
-```python
->>> from transformers import pipeline, set_seed
->>> generator = pipeline('text-generation', model='gpt2')
->>> set_seed(42)
->>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
-[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
- {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
- {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
- {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
- {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
-```
 Here is how to use this model to get the features of a given text in PyTorch:
-```python
-from transformers import GPT2Tokenizer, GPT2Model
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-model = GPT2Model.from_pretrained('gpt2')
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-and in TensorFlow:
-```python
-from transformers import GPT2Tokenizer, TFGPT2Model
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-model = TFGPT2Model.from_pretrained('gpt2')
 text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-### Limitations and bias
-The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
-unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
-[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
-> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
-> that require the generated text to be true.
->
-> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
-> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
-> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
-> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
-> levels of caution around use cases that are sensitive to biases around human attributes.
-Here's an example of how the model can have biased predictions:
-```python
->>> from transformers import pipeline, set_seed
->>> generator = pipeline('text-generation', model='gpt2')
->>> set_seed(42)
->>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
-[{'generated_text': 'The White man worked as a mannequin for'},
- {'generated_text': 'The White man worked as a maniser of the'},
- {'generated_text': 'The White man worked as a bus conductor by day'},
- {'generated_text': 'The White man worked as a plumber at the'},
- {'generated_text': 'The White man worked as a journalist. He had'}]
->>> set_seed(42)
->>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
-[{'generated_text': 'The Black man worked as a man at a restaurant'},
- {'generated_text': 'The Black man worked as a car salesman in a'},
- {'generated_text': 'The Black man worked as a police sergeant at the'},
- {'generated_text': 'The Black man worked as a man-eating monster'},
- {'generated_text': 'The Black man worked as a slave, and was'}]
-```
-This bias will also affect all fine-tuned versions of this model.
-## Training data
-The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
-pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
-this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
-40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
-[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
-## Training procedure
-### Preprocessing
-The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
-vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
-The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
-details of training.
-## Evaluation results
-The model achieves the following results without any fine-tuning (zero-shot):
-| Dataset  | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB    | enwiki8 | text8  | WikiText103 | 1BW   |
-|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
-| (metric) | (PPL)   | (ACC)   | (ACC)  | (ACC)  | (PPL)     | (PPL)  | (BPB)   | (BPC)  | (PPL)       | (PPL) |
-|          | 35.13   | 45.99   | 87.65  | 83.4   | 29.41     | 65.85  | 1.16    | 1,17   | 37.50       | 75.20 |
-### BibTeX entry and citation info
-```bibtex
-@article{radford2019language,
-  title={Language Models are Unsupervised Multitask Learners},
-  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
-  year={2019}
-}
-```
-<a href="https://huggingface.co/exbert/?model=gpt2">
-	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
-</a>

+from transformers import pipeline
+unmasker = pipeline('fill-mask', model='xlm-roberta-base')
+unmasker("Hello I'm a <mask> model.")
+[{'score': 0.10563907772302628,
+  'sequence': "Hello I'm a fashion model.",
+  'token': 54543,
+  'token_str': 'fashion'},
+ {'score': 0.08015287667512894,
+  'sequence': "Hello I'm a new model.",
+  'token': 3525,
+  'token_str': 'new'},
+ {'score': 0.033413201570510864,
+  'sequence': "Hello I'm a model model.",
+  'token': 3299,
+  'token_str': 'model'},
+ {'score': 0.030217764899134636,
+  'sequence': "Hello I'm a French model.",
+  'token': 92265,
+  'token_str': 'French'},
+ {'score': 0.026436051353812218,
+  'sequence': "Hello I'm a sexy model.",
+  'token': 17473,
+  'token_str': 'sexy'}]
 Here is how to use this model to get the features of a given text in PyTorch:
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
+model = AutoModelForMaskedLM.from_pretrained("xlm-roberta-base")
+# prepare input
 text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+# forward pass
+output = model(**encoded_input)
+BibTeX entry and citation info
+@article{DBLP:journals/corr/abs-1911-02116,
+  author    = {Alexis Conneau and
+               Kartikay Khandelwal and
+               Naman Goyal and
+               Vishrav Chaudhary and
+               Guillaume Wenzek and
+               Francisco Guzm{\'{a}}n and
+               Edouard Grave and
+               Myle Ott and
+               Luke Zettlemoyer and
+               Veselin Stoyanov},
+  title     = {Unsupervised Cross-lingual Representation Learning at Scale},
+  journal   = {CoRR},
+  volume    = {abs/1911.02116},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1911.02116},
+  eprinttype = {arXiv},
+  eprint    = {1911.02116},
+  timestamp = {Mon, 11 Nov 2019 18:38:09 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}