Instructions to use florentgbelidji/all-mpnet-base-v2__tweet_eval_emotion__classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use florentgbelidji/all-mpnet-base-v2__tweet_eval_emotion__classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="florentgbelidji/all-mpnet-base-v2__tweet_eval_emotion__classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("florentgbelidji/all-mpnet-base-v2__tweet_eval_emotion__classifier") model = AutoModelForSequenceClassification.from_pretrained("florentgbelidji/all-mpnet-base-v2__tweet_eval_emotion__classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01e7cf71947b0307b55ffcedcc4dec1aeff133a280d954d9ba196c0bf1c7ce49
- Size of remote file:
- 438 MB
- SHA256:
- 47db92168360454d0de6a7275fc7fb3c325341890ef244412d1551a7b76a39b6
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