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language: hi |
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# Releasing Hindi ELECTRA model |
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This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra). |
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**As of 2022 I recommend Google's MuRIL model trained on English, Hindi, and other major Indian languages, both in their script and latinized script**: https://huggingface.co/google/muril-base-cased and https://huggingface.co/google/muril-large-cased |
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**For causal language models, I would suggest https://huggingface.co/sberbank-ai/mGPT, though this is a large model** |
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<a href="https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_">Tokenization and training CoLab</a> |
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I originally used <a href="https://github.com/monsoonNLP/transformers">a modified ELECTRA</a> for finetuning, but now use SimpleTransformers. |
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<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 |
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## Example Notebooks |
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This small model has comparable results to Multilingual BERT on <a href="https://colab.research.google.com/drive/18FQxp9QGOORhMENafQilEmeAo88pqVtP">BBC Hindi news classification</a> |
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and on <a href="https://colab.research.google.com/drive/1UYn5Th8u7xISnPUBf72at1IZIm3LEDWN">Hindi movie reviews / sentiment analysis</a> (using SimpleTransformers) |
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You can get higher accuracy using ktrain by adjusting learning rate (also: changing model_type in config.json - this is an open issue with ktrain): https://colab.research.google.com/drive/1mSeeSfVSOT7e-dVhPlmSsQRvpn6xC05w?usp=sharing |
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Question-answering on MLQA dataset: https://colab.research.google.com/drive/1i6fidh2tItf_-IDkljMuaIGmEU6HT2Ar#scrollTo=IcFoAHgKCUiQ |
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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 |
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does not perform as well on the BBC news classification task. |
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## Corpus |
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Download: https://drive.google.com/drive/folders/1SXzisKq33wuqrwbfp428xeu_hDxXVUUu?usp=sharing |
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The corpus is two files: |
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- Hindi CommonCrawl deduped by OSCAR https://traces1.inria.fr/oscar/ |
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- latest Hindi Wikipedia ( https://dumps.wikimedia.org/hiwiki/ ) + WikiExtractor to txt |
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Bonus notes: |
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- Adding English wiki text or parallel corpus could help with cross-lingual tasks and training |
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## Vocabulary |
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https://drive.google.com/file/d/1-6tXrii3tVxjkbrpSJE9MOG_HhbvP66V/view?usp=sharing |
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Bonus notes: |
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- Created with HuggingFace Tokenizers; you can increase vocabulary size and re-train; remember to change ELECTRA vocab_size |
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## Training |
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Structure your files, with data-dir named "trainer" here |
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``` |
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trainer |
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- vocab.txt |
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- pretrain_tfrecords |
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-- (all .tfrecord... files) |
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- models |
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-- modelname |
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--- checkpoint |
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--- graph.pbtxt |
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--- model.* |
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``` |
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CoLab notebook gives examples of GPU vs. TPU setup |
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[configure_pretraining.py](https://github.com/google-research/electra/blob/master/configure_pretraining.py) |
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## Conversion |
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Use this process to convert an in-progress or completed ELECTRA checkpoint to a Transformers-ready model: |
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``` |
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git clone https://github.com/huggingface/transformers |
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python ./transformers/src/transformers/convert_electra_original_tf_checkpoint_to_pytorch.py |
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--tf_checkpoint_path=./models/checkpointdir |
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--config_file=config.json |
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--pytorch_dump_path=pytorch_model.bin |
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--discriminator_or_generator=discriminator |
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python |
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``` |
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``` |
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from transformers import TFElectraForPreTraining |
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model = TFElectraForPreTraining.from_pretrained("./dir_with_pytorch", from_pt=True) |
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model.save_pretrained("tf") |
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``` |
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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: |
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``` |
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transformers-cli upload directory |
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``` |
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