Instructions to use deepesh0x/autotrain-mlsec-1013333734 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepesh0x/autotrain-mlsec-1013333734 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepesh0x/autotrain-mlsec-1013333734")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-mlsec-1013333734") model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-mlsec-1013333734", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_input.pkl from deepesh0x/autotrain-mlsec-1013333734: direct link, hf CLI and curl.
- Browser
- Download file 9.21 kB
-
https://huggingface.co/deepesh0x/autotrain-mlsec-1013333734/resolve/main/sample_input.pkl
- Command line
-
hf download hf://deepesh0x/autotrain-mlsec-1013333734/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/deepesh0x/autotrain-mlsec-1013333734/resolve/main/sample_input.pkl
9.21 kB
- Xet hash:
- fe307042400f4b92a14ccb398abb8287d58b33a7218d4a9157d59c320d8ab7a9
- Size of remote file:
- 9.21 kB
- SHA256:
- 91989bcca1981a671864a4d06ffa1c8318a1e65cc0425301a45e732298fa9938
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