Text Classification
Transformers
PyTorch
Arabic
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use MMars/marbertv2_flodusta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MMars/marbertv2_flodusta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MMars/marbertv2_flodusta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MMars/marbertv2_flodusta") model = AutoModelForSequenceClassification.from_pretrained("MMars/marbertv2_flodusta") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -16,6 +16,11 @@ metrics:
|
|
| 16 |
- recall
|
| 17 |
- f1
|
| 18 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 19 |
|
| 20 |
# Model Trained Using AutoTrain
|
| 21 |
|
|
|
|
| 16 |
- recall
|
| 17 |
- f1
|
| 18 |
---
|
| 19 |
+
# Labels Mapping
|
| 20 |
+
0 non event
|
| 21 |
+
1 flood
|
| 22 |
+
2 dust storm
|
| 23 |
+
3 traffic accident
|
| 24 |
|
| 25 |
# Model Trained Using AutoTrain
|
| 26 |
|