Instructions to use Mango-Juice/trpg_emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mango-Juice/trpg_emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mango-Juice/trpg_emotion_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mango-Juice/trpg_emotion_classification") model = AutoModelForSequenceClassification.from_pretrained("Mango-Juice/trpg_emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "optimal_thresholds": [ | |
| 0.5, | |
| 0.4, | |
| 0.3, | |
| 0.3, | |
| 0.45, | |
| 0.25, | |
| 0.3, | |
| 0.45, | |
| 0.5, | |
| 0.25, | |
| 0.45, | |
| 0.55, | |
| 0.4, | |
| 0.6000000000000001, | |
| 0.25, | |
| 0.45, | |
| 0.05, | |
| 0.55, | |
| 0.5, | |
| 0.3, | |
| 0.55, | |
| 0.5, | |
| 0.35000000000000003, | |
| 0.35000000000000003, | |
| 0.4, | |
| 0.5, | |
| 0.3, | |
| 0.4 | |
| ], | |
| "threshold_description": "Optimized thresholds for each emotion label for best F1 score" | |
| } |