Instructions to use deman539/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deman539/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deman539/emotion_classification", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deman539/emotion_classification") model = AutoModelForSequenceClassification.from_pretrained("deman539/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42b28a08b45d37ee0c3e44dbcaed4055c0c845f0a99f7f1f96efc60cb5fae030
- Size of remote file:
- 498 MB
- SHA256:
- b97471f79f8fd3f082c973e4b974af924a84605dc831695771614defda43ba01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.