Back to all models
text-classification 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-WNLI
Share Copied link to clipboard

Monthly model downloads

textattack/bert-base-uncased-WNLI textattack/bert-base-uncased-WNLI
83 downloads
last 30 days

pytorch

tf

Contributed by

TextAttack
2 team members · 82 models

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

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

TextAttack Model Card

This bert-base-uncased model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the nlp library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 5e-05, and a maximum sequence length of 256. 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 0.5633802816901409, as measured by the eval set accuracy, found after 1 epoch.

For more information, check out TextAttack on Github.