Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use aidiary/distilbert-base-uncased-finetuned-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aidiary/distilbert-base-uncased-finetuned-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aidiary/distilbert-base-uncased-finetuned-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aidiary/distilbert-base-uncased-finetuned-emotion") model = AutoModelForSequenceClassification.from_pretrained("aidiary/distilbert-base-uncased-finetuned-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fea076f8c69086e7565dbb0c791c8faa3b78f10445b1a221c42a7760ab0378b6
- Size of remote file:
- 268 MB
- SHA256:
- fb54a574bee4dc65c81e6ff5c202d8e41d23b393beda812ca0994184b8134659
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