Text Classification
Transformers
PyTorch
Catalan
roberta
catalan
multi-class-classification
natural-language-understanding
intent-classificaiton
roberta-large
Eval Results (legacy)
text-embeddings-inference
Instructions to use projecte-aina/roberta-large-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-large-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-large-ca-v2-massive")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-large-ca-v2-massive") model = AutoModelForSequenceClassification.from_pretrained("projecte-aina/roberta-large-ca-v2-massive") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54ea040b6ec99cb1cfb84685abe71a8483aea049febb48f0cc5cfeb139b88e52
- Size of remote file:
- 3.12 kB
- SHA256:
- 5fbf2dad39811537bdbc644e56d01264965edf8e2c7d57c4cff5dd19fc1005f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.