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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3eac77527f1e2fb008589b4b07bf9afc86f7f116f37b5c994b1f58347712c839
- Size of remote file:
- 499 MB
- SHA256:
- 3e5726245bcafbf47bba50754f5e6369d8aba2a1b8a7199f0438d13f765ac9cd
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