Instructions to use dima806/text-emotion-classifier-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/text-emotion-classifier-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/text-emotion-classifier-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/text-emotion-classifier-distilbert") model = AutoModelForSequenceClassification.from_pretrained("dima806/text-emotion-classifier-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0f2f083d779438253aa757a7ebfc70cf22e26be1ef35b35fc3e0c15de5bea41
- Size of remote file:
- 438 MB
- SHA256:
- d596fc7f8330f9f91c91bdeacdf18ddbeddae22eaddc2f4e7213ffb92a69a710
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