Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use slickdata/finetuned-Sentiment-classfication-ROBERTA-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slickdata/finetuned-Sentiment-classfication-ROBERTA-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slickdata/finetuned-Sentiment-classfication-ROBERTA-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slickdata/finetuned-Sentiment-classfication-ROBERTA-model") model = AutoModelForSequenceClassification.from_pretrained("slickdata/finetuned-Sentiment-classfication-ROBERTA-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59efeaecaaca849546758d323401df62b4b2b595a6cf5411d36f9cee1054e025
- Size of remote file:
- 499 MB
- SHA256:
- 377bec33e20b91bf7df1f316c3fd71a432f61dc0bca96bf383a1119dd25d0ff8
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