Instructions to use karths/binary_classification_train_perf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_perf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_perf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_perf") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_perf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download top_repo_data.csv from karths/binary_classification_train_perf: direct link, hf CLI and curl.
- Browser
- Download file 10.8 MB
-
https://huggingface.co/karths/binary_classification_train_perf/resolve/main/top_repo_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_perf/top_repo_data.csv
-
curl -L -o top_repo_data.csv https://huggingface.co/karths/binary_classification_train_perf/resolve/main/top_repo_data.csv
10.8 MB
- Xet hash:
- 4cf3a3a3e42bc8cbd36f1494aae85f65cc55433fe4901c465fb8358b3f05344e
- Size of remote file:
- 10.8 MB
- SHA256:
- 132b9bf2bfbc87c122e18c1f166fb90f5589245cd00d07ddf0bd55b64c501e07
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