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 eval_results.json from SotirisLegkas/binary_hate: direct link, hf CLI and curl.
- Browser
- Download file 314 Bytes
-
https://huggingface.co/SotirisLegkas/binary_hate/resolve/main/eval_results.json
- Command line
-
hf download hf://SotirisLegkas/binary_hate/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/SotirisLegkas/binary_hate/resolve/main/eval_results.json
314 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.8966228000634215, | |
| "eval_loss": 0.5619075894355774, | |
| "eval_macro_f1": 0.8966137527188092, | |
| "eval_micro_f1": 0.8966228000634215, | |
| "eval_runtime": 51.7396, | |
| "eval_samples": 12614, | |
| "eval_samples_per_second": 243.798, | |
| "eval_steps_per_second": 15.249 | |
| } |