Instructions to use tweettemposhift/hate-hate_balance_random3_seed2-bernice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tweettemposhift/hate-hate_balance_random3_seed2-bernice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tweettemposhift/hate-hate_balance_random3_seed2-bernice")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/hate-hate_balance_random3_seed2-bernice") model = AutoModelForSequenceClassification.from_pretrained("tweettemposhift/hate-hate_balance_random3_seed2-bernice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download summary.json from tweettemposhift/hate-hate_balance_random3_seed2-bernice: direct link, hf CLI and curl.
- Browser
- Download file 224 Bytes
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https://huggingface.co/tweettemposhift/hate-hate_balance_random3_seed2-bernice/resolve/main/summary.json
- Command line
-
hf download hf://tweettemposhift/hate-hate_balance_random3_seed2-bernice/summary.json
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curl -L -o summary.json https://huggingface.co/tweettemposhift/hate-hate_balance_random3_seed2-bernice/resolve/main/summary.json
224 Bytes
| {"test/eval_loss": 0.7469832301139832, "test/eval_f1": 0.5679012345679012, "test/eval_accuracy": 0.8113207547169812, "test/eval_runtime": 0.8688, "test/eval_samples_per_second": 427.026, "test/eval_steps_per_second": 54.098} |