Instructions to use elozano/tweet_offensive_eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elozano/tweet_offensive_eval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elozano/tweet_offensive_eval")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elozano/tweet_offensive_eval") model = AutoModelForSequenceClassification.from_pretrained("elozano/tweet_offensive_eval", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the offensive config and train split of tweet_eval
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the offensive config and train split of the [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset by @fabeelaalirawther@gmail.com, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-tweet_eval-offensive-93ad2d-30713144953).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=tweet_eval).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=tweet_eval).cac5611
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First model version
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