Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tbasic5/distilbert-base-uncased-finetuned-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tbasic5/distilbert-base-uncased-finetuned-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tbasic5/distilbert-base-uncased-finetuned-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tbasic5/distilbert-base-uncased-finetuned-emotion") model = AutoModelForSequenceClassification.from_pretrained("tbasic5/distilbert-base-uncased-finetuned-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2921f52aca2cbbf7ec198ba12516f05f54bf8dc947a5017730276be0fb145202
- Size of remote file:
- 268 MB
- SHA256:
- 8d08764adb5c4200a81c4b04b7f598d732efaa9192f0d752f8edbffd8d26f24f
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