Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/roberta-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/roberta-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/roberta-classification-10ksamples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/roberta-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/roberta-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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oid sha256:7c5bc0896cf08475db3395e55019519b388cf39fe3993f172840b01c0e338b12
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size 498612824
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