Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use umangchaudhry/bert-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use umangchaudhry/bert-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="umangchaudhry/bert-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("umangchaudhry/bert-emotion") model = AutoModelForSequenceClassification.from_pretrained("umangchaudhry/bert-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7b3f1004f822aeeaba95dda586cdf0b683494b082c229208036779924bbbd7f
- Size of remote file:
- 263 MB
- SHA256:
- fc2711f48b3d02cba1bc90e60a9ed3cabedfe0fbfefc46221c191ebe8c5471b3
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