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:
- 233510569411a0d38e957298be76b1e80728f54795bcdbe78091e5d389de87bc
- Size of remote file:
- 3.25 kB
- SHA256:
- d761d15e30ac5d941887599a29b65291a6a650292114ef254066778c28f51c1a
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