Text Classification
Transformers
Safetensors
modernbert
multi_label_classification
Generated from Trainer
text-embeddings-inference
Instructions to use ATL1978/finetuned_model_emotion_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ATL1978/finetuned_model_emotion_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ATL1978/finetuned_model_emotion_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ATL1978/finetuned_model_emotion_detection") model = AutoModelForSequenceClassification.from_pretrained("ATL1978/finetuned_model_emotion_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1044130a15b64f3b433c82a2ae5584ee105ac529a4682a8ee5656bb305c107fe
- Size of remote file:
- 5.2 kB
- SHA256:
- 5eeb5858bf4c59ad3bad9da2e09292751034313ad74f8cf109e964b528403dd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.