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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -90,8 +90,8 @@ from pprint import pprint
|
|
| 90 |
nlp = pipeline("text-classification", model="https://huggingface.co/projecte-aina/roberta-base-ca-v2-massive")
|
| 91 |
example = "m'agraden les cançons del serrat"
|
| 92 |
|
| 93 |
-
|
| 94 |
-
pprint(
|
| 95 |
```
|
| 96 |
|
| 97 |
## Limitations and bias
|
|
|
|
| 90 |
nlp = pipeline("text-classification", model="https://huggingface.co/projecte-aina/roberta-base-ca-v2-massive")
|
| 91 |
example = "m'agraden les cançons del serrat"
|
| 92 |
|
| 93 |
+
intent = nlp(example)
|
| 94 |
+
pprint(intent)
|
| 95 |
```
|
| 96 |
|
| 97 |
## Limitations and bias
|