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https://api-inference.huggingface.co/models/textattack/bert-base-uncased-rotten-tomatoes
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textattack/bert-base-uncased-rotten-tomatoes textattack/bert-base-uncased-rotten-tomatoes
1,067 downloads
last 30 days

pytorch

tf

Contributed by

TextAttack
3 team members · 84 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-rotten-tomatoes") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-rotten-tomatoes")
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TextAttack Model Card

This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack 
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned 
for 10 epochs with a batch size of 16, a learning 
rate of 2e-05, and a maximum sequence length of 128. 
Since this was a classification task, the model was trained with a cross-entropy loss function. 
The best score the model achieved on this task was 0.875234521575985, as measured by the 
eval set accuracy, found after 4 epochs.

For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).