Instructions to use Sohaibsoussi/distilbert-base-uncased-finetuned-emotion-by-sohsou with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sohaibsoussi/distilbert-base-uncased-finetuned-emotion-by-sohsou with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sohaibsoussi/distilbert-base-uncased-finetuned-emotion-by-sohsou")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sohaibsoussi/distilbert-base-uncased-finetuned-emotion-by-sohsou") model = AutoModelForSequenceClassification.from_pretrained("Sohaibsoussi/distilbert-base-uncased-finetuned-emotion-by-sohsou") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abff4aca31880ca7e3c1aa05474004b74c5d9a7d35e88df3b793a01d0349ae37
- Size of remote file:
- 268 MB
- SHA256:
- d38684ff8e53f845ae0684d7c1f3184d9417431cba62ba4a40bb551ccb2d94f6
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