Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use postgrammar/distilbert-base-uncased-finetuned-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use postgrammar/distilbert-base-uncased-finetuned-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="postgrammar/distilbert-base-uncased-finetuned-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("postgrammar/distilbert-base-uncased-finetuned-emotion") model = AutoModelForSequenceClassification.from_pretrained("postgrammar/distilbert-base-uncased-finetuned-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 098da45da74deb2e5859d263db7e50ad9ac7036fbd91d59b81b56764f833b0f2
- Size of remote file:
- 268 MB
- SHA256:
- f2e94754861087f8ac959dc739a94acd3da9eae7ab1323a919bdffeb2ae1f32c
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