Instructions to use tminhtri1910/bert-mrpc-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tminhtri1910/bert-mrpc-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tminhtri1910/bert-mrpc-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tminhtri1910/bert-mrpc-analysis") model = AutoModelForSequenceClassification.from_pretrained("tminhtri1910/bert-mrpc-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9de654cd228c5e522a9ca0a64834277df1131435681c4ccd23504e6fac7666f
- Size of remote file:
- 5.84 kB
- SHA256:
- b8e504f02c42697b7c1b4a091b343bf3e8b9c34247fe5af53d68f14381dbe3a0
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