Instructions to use Osiris/emotion_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Osiris/emotion_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Osiris/emotion_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Osiris/emotion_classifier") model = AutoModelForSequenceClassification.from_pretrained("Osiris/emotion_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 476 Bytes
6a75446 7c20f23 6a75446 fb64d0c 6a75446 fb64d0c 6a75446 c0c0187 6a75446 531104b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ### Introduction:
This model belongs to text-classification. You can determine the emotion behind a sentence.
### Label Explaination:
LABEL_0: Positive (have positive emotion)
LABEL_1: Negative (have negative emotion)
### Usage:
```python
>>> from transformers import pipeline
>>> ec = pipeline('text-classification', model='Osiris/emotion_classifier')
>>> ec("Hello, I'm a good model.")
```
### Accuracy:
We reach 83.82% for validation dataset, and 84.42% for test dataset. |