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-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-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-cased-sts")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-cased-sts") model = AutoModelForSequenceClassification.from_pretrained("projecte-aina/roberta-base-ca-cased-sts") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df306d0a36369057f82e316a5f37dda229a19b4a5340d586ed90a6afcfc1640f
- Size of remote file:
- 2.8 kB
- SHA256:
- b082c17858e6b0b991611988c93aaae3cc138a67f2ed1507e280b40332e18502
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.