# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-rotten_tomatoes")
model = AutoModelForMaskedLM.from_pretrained("textattack/bert-base-uncased-rotten_tomatoes")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset
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 64, a learning
rate of 5e-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).
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="textattack/bert-base-uncased-rotten_tomatoes")