Instructions to use karths/binary_classification_train_infrastructure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_infrastructure with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_infrastructure")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_infrastructure") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_infrastructure", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download top_repo_data.csv from karths/binary_classification_train_infrastructure: direct link, hf CLI and curl.
- Browser
- Download file 16.7 MB
-
https://huggingface.co/karths/binary_classification_train_infrastructure/resolve/main/top_repo_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_infrastructure/top_repo_data.csv
-
curl -L -o top_repo_data.csv https://huggingface.co/karths/binary_classification_train_infrastructure/resolve/main/top_repo_data.csv
16.7 MB
- Xet hash:
- d684b753f20e6b0aac3857a1ef684a3adf992dc6eba1151d728c67385c7bb461
- Size of remote file:
- 16.7 MB
- SHA256:
- f7d1e7c1ad91e0f17c3dcf36623fb95662d01597d29f07fcf73845a72bc630c7
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