Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jnieus01/bert-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnieus01/bert-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jnieus01/bert-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jnieus01/bert-emotion") model = AutoModelForSequenceClassification.from_pretrained("jnieus01/bert-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2499f78e355e3c7bd809c8745525ee996fa54248a579338fdf511be1c6af1f0c
- Size of remote file:
- 263 MB
- SHA256:
- b8a55812dd645e02a6339fe4461a18eca312c98f3f7bbca0134f60c8d6a23b4e
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