Instructions to use SotirisLegkas/binary_hate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SotirisLegkas/binary_hate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SotirisLegkas/binary_hate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SotirisLegkas/binary_hate") model = AutoModelForSequenceClassification.from_pretrained("SotirisLegkas/binary_hate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_results.json from SotirisLegkas/binary_hate: direct link, hf CLI and curl.
- Browser
- Download file 319 Bytes
-
https://huggingface.co/SotirisLegkas/binary_hate/resolve/main/predict_results.json
- Command line
-
hf download hf://SotirisLegkas/binary_hate/predict_results.json
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curl -L -o predict_results.json https://huggingface.co/SotirisLegkas/binary_hate/resolve/main/predict_results.json
319 Bytes
| { | |
| "predict_accuracy": 0.8885277095060652, | |
| "predict_loss": 0.6036547422409058, | |
| "predict_macro_f1": 0.8885193839056237, | |
| "predict_micro_f1": 0.8885277095060652, | |
| "predict_runtime": 51.6751, | |
| "predict_samples": 12613, | |
| "predict_samples_per_second": 244.083, | |
| "predict_steps_per_second": 15.268 | |
| } |