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:
- 271cb841b0101b9808a33a77d364c78483901bf581155a5255dc43114b0c7619
- Size of remote file:
- 268 MB
- SHA256:
- 20a05e7310bde030e570d4ed92a10a718a22c16ee577cb62f94f26959a061195
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