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:
- ec0116adf155c2d4d9d960495070d5a92079e95eed18c268eb3efbd21d47620b
- Size of remote file:
- 4.03 kB
- SHA256:
- 52dfcc909acf6b9e8f83708dba203022e546905e2e1c7118d39bde8354b92d87
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