Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DerivedFunction1/roberta-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DerivedFunction1/roberta-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DerivedFunction1/roberta-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DerivedFunction1/roberta-v2") model = AutoModelForSequenceClassification.from_pretrained("DerivedFunction1/roberta-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f41afd788325a29cf84af41288c1c901296493032834752afa113245a06b6da5
- Size of remote file:
- 499 MB
- SHA256:
- 39ac4d6ff88561def42ecb139a7778bcc8483d5d09338a1dcf04acd725b1ba1b
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