Text Classification
Transformers
PyTorch
English
roberta
sentiment-analysis
sentiment-classification
targeted-sentiment-classification
target-depentent-sentiment-classification
Instructions to use fhamborg/roberta-targeted-sentiment-classification-newsarticles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fhamborg/roberta-targeted-sentiment-classification-newsarticles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fhamborg/roberta-targeted-sentiment-classification-newsarticles")# Load model directly from transformers import AutoTokenizer, PretrainedWrapper tokenizer = AutoTokenizer.from_pretrained("fhamborg/roberta-targeted-sentiment-classification-newsarticles") model = PretrainedWrapper.from_pretrained("fhamborg/roberta-targeted-sentiment-classification-newsarticles") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c23a188f020b27a9a12e74b52ca4c7ae0be35bf4dca62ff60f2c5f7fb460495
- Size of remote file:
- 612 MB
- SHA256:
- 3767259604cbaf2b391a45007b063e9eefaf5cd668f610334e9a0593314efb99
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