Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use gArthur98/Roberta-classweight-Sentiment-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gArthur98/Roberta-classweight-Sentiment-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gArthur98/Roberta-classweight-Sentiment-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gArthur98/Roberta-classweight-Sentiment-classifier") model = AutoModelForSequenceClassification.from_pretrained("gArthur98/Roberta-classweight-Sentiment-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60bd4eaec680ba3a68a898d065befccf92d1dd45ad22acb04dbc32ffcaf9eac5
- Size of remote file:
- 4.03 kB
- SHA256:
- a9119b88cc544b8949cb1ff9039f84329aa1b29a9e83e8290ee7b8d027308811
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.