Text Classification
Transformers
PyTorch
bert
Issue_fixed
textattack
textclassification
entailment
text-embeddings-inference
Instructions to use chromeNLP/textattack_bert_base_MNLI_fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chromeNLP/textattack_bert_base_MNLI_fixed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chromeNLP/textattack_bert_base_MNLI_fixed")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chromeNLP/textattack_bert_base_MNLI_fixed") model = AutoModelForSequenceClassification.from_pretrained("chromeNLP/textattack_bert_base_MNLI_fixed") - Notebooks
- Google Colab
- Kaggle
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README.md
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Fixed label mapping issue for textattack/bert-base-uncased-MNLI, if using the original model, the predicted label has systematic confusion with the huggingface MNLI dataset. See the Github issue: https://github.com/QData/TextAttack/issues/684
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Fixed label mapping issue for textattack/bert-base-uncased-MNLI, if using the original model, the predicted label has systematic confusion with the huggingface MNLI dataset. See the Github issue: https://github.com/QData/TextAttack/issues/684. The fixed accuracy_mm is 84.44% and is 7% before the fix applied.
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