Text Classification
Transformers
PyTorch
Catalan
roberta
catalan
multi-class-classification
natural-language-understanding
intent-classificaiton
Eval Results (legacy)
text-embeddings-inference
Instructions to use projecte-aina/roberta-base-ca-v2-massive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/roberta-base-ca-v2-massive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-massive")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-v2-massive") model = AutoModelForSequenceClassification.from_pretrained("projecte-aina/roberta-base-ca-v2-massive") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -111,7 +111,7 @@ The model was trained with a batch size of 16 and a learning rate of 5e-5 for 20
|
|
| 111 |
This model was finetuned maximizing the weighted F1 score.
|
| 112 |
|
| 113 |
### Evaluation results
|
| 114 |
-
We evaluated the _roberta-base-ca-v2-massive_ on the MASSIVE test set obtaining a weighted F1 score of 87.
|
| 115 |
|
| 116 |
## Additional information
|
| 117 |
|
|
|
|
| 111 |
This model was finetuned maximizing the weighted F1 score.
|
| 112 |
|
| 113 |
### Evaluation results
|
| 114 |
+
We evaluated the _roberta-base-ca-v2-massive_ on the MASSIVE test set obtaining a weighted F1 score of 87.03.
|
| 115 |
|
| 116 |
## Additional information
|
| 117 |
|