How to Fine-Tune BERT for Text Classification?
Paper • 1905.05583 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AbstractQbit/electra_large_imdb_htsplice")
model = AutoModelForSequenceClassification.from_pretrained("AbstractQbit/electra_large_imdb_htsplice")Quick Links
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Check out the documentation for more information.
google/electra-large-discriminator finetuned on imdb dataset for 2 epoches.
Large examples tokenized with head and tail parts of a review, as described in How to Fine-Tune BERT for Text Classification?
def preprocess_function(example):
tokens = tokenizer(example["text"], truncation=False)
if len(tokens['input_ids']) > 512:
tokens['input_ids'] = tokens['input_ids'][:129] + \
[102] + tokens['input_ids'][-382:]
tokens['token_type_ids'] = [0]*512
tokens['attention_mask'] = [1]*512
return tokens
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AbstractQbit/electra_large_imdb_htsplice")