Update README.md

README.md

@@ -4,13 +4,25 @@ language: hi
 
 # Releasing Hindi ELECTRA model
 
-This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra). **I don't modify ELECTRA until we get into finetuning**
+This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra).
+
+**Consider using this newer, larger model: https://huggingface.co/monsoon-nlp/hindi-tpu-electra**
 
 <a href="https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_">Tokenization and training CoLab</a>
 
-<a href="https://medium.com/@mapmeld/teaching-hindi-to-electra-b11084baab81">Blog post</a>
+I originally used <a href="https://github.com/monsoonNLP/transformers">a modified ELECTRA</a> for finetuning, but now use SimpleTransformers.
+
+<a href="https://medium.com/@mapmeld/teaching-hindi-to-electra-b11084baab81">Blog post</a> - I was greatly influenced by: https://huggingface.co/blog/how-to-train
+
+## Example Notebooks
+
+This small model has comparable results to Multilingual BERT on <a href="https://colab.research.google.com/drive/18FQxp9QGOORhMENafQilEmeAo88pqVtP">BBC Hindi news classification</a>
+and on <a href="https://colab.research.google.com/drive/1UYn5Th8u7xISnPUBf72at1IZIm3LEDWN">Hindi movie reviews / sentiment analysis</a> (using SimpleTransformers)
 
-I was greatly influenced by: https://huggingface.co/blog/how-to-train
+Question-answering on MLQA dataset: https://colab.research.google.com/drive/1i6fidh2tItf_-IDkljMuaIGmEU6HT2Ar#scrollTo=IcFoAHgKCUiQ
+
+A larger model (<a href="https://huggingface.co/monsoon-nlp/hindi-tpu-electra">Hindi-TPU-Electra</a>) using ELECTRA base size outperforms both models on Hindi movie reviews / sentiment analysis, but
+does not perform as well on the BBC news classification task.
 
 ## Corpus
 
@@ -28,7 +40,7 @@ Bonus notes:
 https://drive.google.com/file/d/1-6tXrii3tVxjkbrpSJE9MOG_HhbvP66V/view?usp=sharing
 
 Bonus notes:
-- Created with HuggingFace Tokenizers; could be longer or shorter, review ELECTRA vocab_size param
+- Created with HuggingFace Tokenizers; you can increase vocabulary size and re-train; remember to change ELECTRA vocab_size
 
 ## Training
 
@@ -50,8 +62,28 @@ CoLab notebook gives examples of GPU vs. TPU setup
 
 [configure_pretraining.py](https://github.com/google-research/electra/blob/master/configure_pretraining.py)
 
-## Using this model with Transformers
+## Conversion
+
+Use this process to convert an in-progress or completed ELECTRA checkpoint to a Transformers-ready model:
+
+```
+git clone https://github.com/huggingface/transformers
+python ./transformers/src/transformers/convert_electra_original_tf_checkpoint_to_pytorch.py
+ --tf_checkpoint_path=./models/checkpointdir
+ --config_file=config.json
+ --pytorch_dump_path=pytorch_model.bin
+ --discriminator_or_generator=discriminator
+python
+```
 
-Sample movie reviews classifier: https://colab.research.google.com/drive/1mSeeSfVSOT7e-dVhPlmSsQRvpn6xC05w
+```
+from transformers import TFElectraForPreTraining
+model = TFElectraForPreTraining.from_pretrained("./dir_with_pytorch", from_pt=True)
+model.save_pretrained("tf")
+```
 
-Slightly outperforms Multilingual BERT on these Hindi Movie Reviews from https://github.com/sid573/Hindi_Sentiment_Analysis
+Once you have formed one directory with config.json, pytorch_model.bin, tf_model.h5, special_tokens_map.json, tokenizer_config.json, and vocab.txt on the same level, run:
+
+```
+transformers-cli upload directory
+```