Back to all models
fill-mask mask_token: [MASK]
Query this model
🔥 This model is currently loaded and running on the Inference API. ⚠️ This model could not be loaded by the inference API. ⚠️ This model can be loaded on the Inference API on-demand.
JSON Output
API endpoint
								$ curl -X POST \
https://api-inference.huggingface.co/models/textattack/bert-base-uncased-rotten-tomatoes
Share Copied link to clipboard

Monthly model downloads

textattack/bert-base-uncased-rotten-tomatoes textattack/bert-base-uncased-rotten-tomatoes
1,807 downloads
last 30 days

pytorch

tf

Contributed by

TextAttack
2 team members · 60 models

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

			
Copy to clipboard
from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-rotten-tomatoes") model = AutoModelWithLMHead.from_pretrained("textattack/bert-base-uncased-rotten-tomatoes")
Uploaded in S3

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 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).