Create README.md

README.md

@@ -0,0 +1,108 @@
+---
+license: apache-2.0
+tags:
+datasets:
+- wikipedia
+- c4
+---
+
+# Perceiver IO for language
+
+Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in [BERT](https://arxiv.org/abs/1810.04805) using a large text corpus obtained by combining [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/perceiver). 
+
+Disclaimer: The team releasing Perceiver IO did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a set of not-too large latent vectors, and only use the inputs to perform cross-attention with the latents. This allows for the time and memory requirements of the self-attention mechanism to not depend on the size of the inputs. 
+
+To decode, the authors employ so-called decoder queries, which allow to flexibly decode the final hidden states of the latents to produce outputs of arbitrary size and semantics. For masked language modeling, the output is a tensor containing the prediction scores of the language modeling head, of shape (batch_size, seq_length, vocab_size).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg" alt="drawing" width="600"/>
+
+<small> Perceiver IO architecture.</small>
+
+As the time and memory requirements of the self-attention mechanism don't depend on the size of the inputs, the Perceiver IO authors train the model directly on raw UTF-8 bytes, rather than on subwords as is done in models like BERT, RoBERTa and GPT-2. This has many benefits: one doesn't need to train a tokenizer before training the model, one doesn't need to maintain a (fixed) vocabulary file, and this also doesn't hurt model performance as shown by [Bostrom et al., 2020](https://arxiv.org/abs/2004.03720).
+
+By pre-training the model, it learns an inner representation of language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
+classifier using the features produced by the BERT model as inputs. 
+
+## Intended uses & limitations
+
+You can use the raw model for masked language modeling, but the model is intended to be fine-tuned on a labeled dataset. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model in PyTorch:
+
+```python
+from transformers import PerceiverTokenizer, PerceiverForMaskedLM
+
+tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
+model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver")
+
+text = "This is an incomplete sentence where some words are missing."
+# prepare input
+encoding = tokenizer(text, padding="max_length", return_tensors="pt")
+# mask " missing.". Note that the model performs much better if the masked span starts with a space.
+encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
+inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device)
+
+# forward pass
+outputs = model(inputs=inputs, attention_mask=input_mask)
+logits = outputs.logits
+masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1)
+print(tokenizer.decode(masked_tokens_predictions))
+>>> should print " missing."
+```
+
+## Training data
+
+This model was pretrained on a combination of [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). 70% of the training tokens were sampled from the C4 dataset and the remaining 30% from Wikipedia. The authors concatenate 10 documents before splitting into crops to reduce wasteful computation on padding tokens. 
+
+## Training procedure
+
+### Preprocessing
+
+Text preprocessing is trivial: it only involves encoding text into UTF-8 bytes, and padding them up to the same length (2048).
+
+### Pretraining
+
+Hyperparameter details can be found in table 9 of the [paper](https://arxiv.org/abs/2107.14795).
+
+## Evaluation results
+
+This model is able to achieve an average score of 81.8 on GLUE. For more details, we refer to table 3 f the original paper. 
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2107-14795,
+  author    = {Andrew Jaegle and
+               Sebastian Borgeaud and
+               Jean{-}Baptiste Alayrac and
+               Carl Doersch and
+               Catalin Ionescu and
+               David Ding and
+               Skanda Koppula and
+               Daniel Zoran and
+               Andrew Brock and
+               Evan Shelhamer and
+               Olivier J. H{\'{e}}naff and
+               Matthew M. Botvinick and
+               Andrew Zisserman and
+               Oriol Vinyals and
+               Jo{\~{a}}o Carreira},
+  title     = {Perceiver {IO:} {A} General Architecture for Structured Inputs {\&}
+               Outputs},
+  journal   = {CoRR},
+  volume    = {abs/2107.14795},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2107.14795},
+  eprinttype = {arXiv},
+  eprint    = {2107.14795},
+  timestamp = {Tue, 03 Aug 2021 14:53:34 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2107-14795.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```