Text Classification
Transformers
PyTorch
Catalan
roberta
catalan
semantic textual similarity
sts-ca
CaText
Catalan Textual Corpus
Eval Results (legacy)
text-embeddings-inference
Instructions to use projecte-aina/roberta-base-ca-v2-cased-sts 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-cased-sts 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-cased-sts")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-sts") model = AutoModelForSequenceClassification.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-sts") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e39c998ce625ec975ce82a45539bf80b235dce3ae2d56c364dfb9ced6e2d9a8
- Size of remote file:
- 499 MB
- SHA256:
- 975baf008a16b632ff4df7c422bd26f02b3ba49291552521ab482a6f837ac1d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.