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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/funnel-transformer/medium/README.md

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+ ---
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+ language: en
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+ license: apache-2.0
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+ datasets:
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+ - bookcorpus
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+ - wikipedia
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+ - gigaword
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+ ---
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+
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+ # Funnel Transformer medium model (B6-3x2-3x2 with decoder)
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+
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+ Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
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+ [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
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+ [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
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+ between english and English.
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+
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+ Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
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+ written by the Hugging Face team.
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+
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+ ## Model description
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+
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+ Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
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+ was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
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+ publicly available data) with an automatic process to generate inputs and labels from those texts.
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+
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+ More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
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+ the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
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+
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+ This way, the model learns an inner representation of the English language that can then be used to extract features
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+ useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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+ classifier using the features produced by the BERT model as inputs.
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+
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+ ## Intended uses & limitations
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+
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+ You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
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+ be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
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+ fine-tuned versions on a task that interests you.
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+
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+ Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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+ to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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+ generation you should look at model like GPT2.
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+
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+ ### How to use
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+
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+
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+ Here is how to use this model to get the features of a given text in PyTorch:
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+
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+ ```python
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+ from transformers import FunnelTokenizer, FunnelModel
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+ tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium")
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+ model = FunneModel.from_pretrained("funnel-transformer/medium")
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+ text = "Replace me by any text you'd like."
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+ encoded_input = tokenizer(text, return_tensors='pt')
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+ output = model(**encoded_input)
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+ ```
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+
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+ and in TensorFlow:
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+
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+ ```python
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+ from transformers import FunnelTokenizer, TFFunnelModel
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+ tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium")
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+ model = TFFunnelModel.from_pretrained("funnel-transformer/medium")
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+ text = "Replace me by any text you'd like."
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+ encoded_input = tokenizer(text, return_tensors='tf')
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+ output = model(encoded_input)
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+ ```
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+
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+ ## Training data
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+
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+ The BERT model was pretrained on:
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+ - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
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+ - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
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+ - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
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+ - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
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+ - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
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+
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @misc{dai2020funneltransformer,
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+ title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
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+ author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
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+ year={2020},
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+ eprint={2006.03236},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG}
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+ }
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+ ```
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+