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
| {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}} |