Instructions to use akshara23/Terra-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akshara23/Terra-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akshara23/Terra-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akshara23/Terra-Classification") model = AutoModelForSequenceClassification.from_pretrained("akshara23/Terra-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from akshara23/Terra-Classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/akshara23/Terra-Classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://akshara23/Terra-Classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/akshara23/Terra-Classification/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- b20ee162e40f9ca781696421e906c8772b8aea6016875f093b1467c61f4e28c9
- Size of remote file:
- 268 MB
- SHA256:
- 36043de5371c213df1a88e49d451506137b11be41bed203549033170d95acbea
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