# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-ag-news")
model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-ag-news")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
TextAttack Model CardThis bert-base-uncased model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the nlp library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-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 0.9514473684210526, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out TextAttack on Github.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-ag-news")