BERT Model Example #32
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Line #10. hash_vocab('bert-base-cased-vocab.txt', 'voc_hash.txt')
Todo Figure out if we can get a download link for this.
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Line #16. df = getDF('/nvme/1/ssayyah/nv-wip/amazon_bookreview.json.gz')
Todo Figure out if we can get a download link for this.
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Line #5. bert = AutoModel.from_pretrained('bert-base-uncased')
Replace with this model :
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
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Can we do it to a fixed length to keep the example minimal , so something like , maybe something like 256 ?
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Line #1. train_seq = torch.tensor(tokens_train['input_ids'])
This goes cuda array interface rather than dlpack . Are there performance implications of either ?
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First PR draft of an example of using cuML BERT tokenizer and model for sentiment classification on Amazon book review dataset.