Instructions to use JahnaviKumar/FGL_DevEmotionAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JahnaviKumar/FGL_DevEmotionAnalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JahnaviKumar/FGL_DevEmotionAnalysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JahnaviKumar/FGL_DevEmotionAnalysis") model = AutoModelForSequenceClassification.from_pretrained("JahnaviKumar/FGL_DevEmotionAnalysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54e1d8b3660759a4acc8983ec1c17ad69dec85cc45803418746ae8ce34863477
- Size of remote file:
- 329 MB
- SHA256:
- 24083a5ed5b1385a04b635d4e9a2029a860b00d9c193c62069676c885b17e9e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.