Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use rendchevi/roberta-base-pr_tqacd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendchevi/roberta-base-pr_tqacd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rendchevi/roberta-base-pr_tqacd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rendchevi/roberta-base-pr_tqacd") model = AutoModelForSequenceClassification.from_pretrained("rendchevi/roberta-base-pr_tqacd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1660b9e35c911d537512f1afe1cc03b7fdd596c70b92f91985ffe98279df3f93
- Size of remote file:
- 5.84 kB
- SHA256:
- 3701c6f3ea8213d0578971abf1c2b3573dbd895dbed6a410efafeb2039b83c2e
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