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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23729e515529a89da4371f8bf9e8ad6caa55cbe468eb5676afa0357666fb3963
- Size of remote file:
- 2.16 kB
- SHA256:
- a254558d12daf5aa3dccf3ded2487f604613304bedb058181049398df83bf1ff
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