Instructions to use YagiASAFAS/MSPoliBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YagiASAFAS/MSPoliBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YagiASAFAS/MSPoliBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YagiASAFAS/MSPoliBERT") model = AutoModelForSequenceClassification.from_pretrained("YagiASAFAS/MSPoliBERT") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -45,6 +45,20 @@ The following hyperparameters were used during training:
|
|
| 45 |
- num_epochs: 8
|
| 46 |
- mixed_precision_training: Native AMP
|
| 47 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 48 |
### Training results
|
| 49 |
|
| 50 |
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
|
|
|
| 45 |
- num_epochs: 8
|
| 46 |
- mixed_precision_training: Native AMP
|
| 47 |
|
| 48 |
+
### Label Mappings
|
| 49 |
+
- 0: Economic Concerns
|
| 50 |
+
- 1: Racial discrimination or polarization
|
| 51 |
+
- 2: Leadership weaknesses
|
| 52 |
+
- 3: Development and infrastructure gaps
|
| 53 |
+
- 4: Corruption
|
| 54 |
+
- 5: Political instablility
|
| 55 |
+
- 6: Socials and Public safety
|
| 56 |
+
- 7: Administration
|
| 57 |
+
- 8: Education
|
| 58 |
+
- 9: Religion issues
|
| 59 |
+
- 10: Environmental
|
| 60 |
+
- 11: Others
|
| 61 |
+
|
| 62 |
### Training results
|
| 63 |
|
| 64 |
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|