Instructions to use Data-Lab/marat_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Lab/marat_model_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Data-Lab/marat_model_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Data-Lab/marat_model_test") model = AutoModelForSequenceClassification.from_pretrained("Data-Lab/marat_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download marat_model_test-encoder.npy from Data-Lab/marat_model_test: direct link, hf CLI and curl.
- Browser
- Download file 7.33 kB
-
https://huggingface.co/Data-Lab/marat_model_test/resolve/main/marat_model_test-encoder.npy
- Command line
-
hf download hf://Data-Lab/marat_model_test/marat_model_test-encoder.npy
-
curl -L -o marat_model_test-encoder.npy https://huggingface.co/Data-Lab/marat_model_test/resolve/main/marat_model_test-encoder.npy
7.33 kB
- Xet hash:
- 7e540c67a235316b959337c97e7a48c73fd6b7a8795b270a4dea1b44ec0093fb
- Size of remote file:
- 7.33 kB
- SHA256:
- d0f66f2f61acf0c5bb288332479ef1da16831562a64f797364f1ef9acc343d7d
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