Instructions to use pmthangk09/bert-base-uncased-esnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pmthangk09/bert-base-uncased-esnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pmthangk09/bert-base-uncased-esnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pmthangk09/bert-base-uncased-esnli") model = AutoModelForSequenceClassification.from_pretrained("pmthangk09/bert-base-uncased-esnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19b05344a03b3b24629a234cacdde2c49d35b411aa014cbb21a2ec31f298d425
- Size of remote file:
- 438 MB
- SHA256:
- 121a54f87916d18f979ed42893e50f9c101794b59cb9ccec441cc956a13d8a17
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