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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bsc-temu commited on
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README.md
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@@ -68,9 +68,7 @@ We evaluated the _roberta-base-ca-cased-sts_ on the STS-ca test set against stan
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| XLM-RoBERTa | 75.40 |
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| WikiBERT-ca | 77.18 |
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| Semantic Textual Similarity | roberta-base-ca-cased-sts | **81.20** |
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For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/berta).
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## Citing
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| XLM-RoBERTa | 75.40 |
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| WikiBERT-ca | 77.18 |
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For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/berta).
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## Citing
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