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 test_data_for_future_evaluation.csv from karths/binary_classification_train_infrastructure: direct link, hf CLI and curl.
- Browser
- Download file 34.1 MB
-
https://huggingface.co/karths/binary_classification_train_infrastructure/resolve/main/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_infrastructure/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_infrastructure/resolve/main/test_data_for_future_evaluation.csv
34.1 MB
- Xet hash:
- 92955c1a1b3dc26b5888bf16179b6be4df47ea836a66c2058637bed58f80740a
- Size of remote file:
- 34.1 MB
- SHA256:
- 5adc72120c0ab5d993faaa58eb13ce09f017f84bfe2c1e3d747e1394ff133a77
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