First draft of model card

README.md

@@ -0,0 +1,80 @@
+---
+license: apache-2.0
+tags:
+- image-classification
+datasets:
+- imagenet
+- imagenet-21k
+---
+
+# CANINE-s (CANINE pre-trained with subword loss) 
+
+TODO
+
+Disclaimer: The team releasing CANINE did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+TODO
+
+## Intended uses & limitations
+
+TODO
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from transformers import CanineTokenizer, CanineModel
+
+model = CanineModel.from_pretrained('google/canine-s')
+tokenizer = CanineTokenizer.from_pretrained('google/canine-s')
+
+inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
+encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
+
+outputs = model(**encoding) # forward pass
+pooled_output = outputs.pooler_output
+sequence_output = outputs.last_hidden_state
+```
+
+## Training data
+
+TODO
+
+## Training procedure
+
+### Preprocessing
+
+TODO
+
+### Pretraining
+
+TODO
+
+## Evaluation results
+
+TODO
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2103-06874,
+  author    = {Jonathan H. Clark and
+               Dan Garrette and
+               Iulia Turc and
+               John Wieting},
+  title     = {{CANINE:} Pre-training an Efficient Tokenization-Free Encoder for
+               Language Representation},
+  journal   = {CoRR},
+  volume    = {abs/2103.06874},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2103.06874},
+  archivePrefix = {arXiv},
+  eprint    = {2103.06874},
+  timestamp = {Tue, 16 Mar 2021 11:26:59 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-06874.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```