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:
- 8ad9105f35b848c265241c69d2b9f7bf50d9744ebce8dbbcdd7a3d48c0479a58
- Size of remote file:
- 438 MB
- SHA256:
- 7dad671f3ed851e4af62587cad6d753c051a8b613bcc90ee2e8d64c4f4ab3396
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.