Instructions to use MilaNLProc/xlm-emo-t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MilaNLProc/xlm-emo-t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MilaNLProc/xlm-emo-t")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/xlm-emo-t") model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/xlm-emo-t", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -16,6 +16,10 @@ widget:
|
|
| 16 |
|
| 17 |
---
|
| 18 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 19 |
## Citation
|
| 20 |
Please use the following bibtex entry if you use this model in your project:
|
| 21 |
```
|
|
|
|
| 16 |
|
| 17 |
---
|
| 18 |
|
| 19 |
+
## Result
|
| 20 |
+
|
| 21 |
+
This model had an F1 of 0.85 on the test set.
|
| 22 |
+
|
| 23 |
## Citation
|
| 24 |
Please use the following bibtex entry if you use this model in your project:
|
| 25 |
```
|