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 reduced_main_data.csv from karths/binary_classification_train_infrastructure: direct link, hf CLI and curl.
- Browser
- Download file 184 MB
-
https://huggingface.co/karths/binary_classification_train_infrastructure/resolve/main/reduced_main_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_infrastructure/reduced_main_data.csv
-
curl -L -o reduced_main_data.csv https://huggingface.co/karths/binary_classification_train_infrastructure/resolve/main/reduced_main_data.csv
184 MB
- Xet hash:
- b6ee23f08a6badd49bf6550fa3c244c2c94e63badefba11906fbcf5616b9456b
- Size of remote file:
- 184 MB
- SHA256:
- 2a3d625b859cda8eaa62ee6e0c95f11b84b081fc55939975bf2398feb8698248
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