Instructions to use philschmid/distilbert-base-uncased-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/distilbert-base-uncased-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/distilbert-base-uncased-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-base-uncased-emotion") model = AutoModelForSequenceClassification.from_pretrained("philschmid/distilbert-base-uncased-emotion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80e87e908d165644f56d0cf8e0a9b91f73e50483f277b45ec9abfd4fca63b1f1
- Size of remote file:
- 268 MB
- SHA256:
- 5aa7398d830fcc94f95af88d7cc3013813668cfc58a07d75a8116cfd8af75c4d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.