Instructions to use pawlo2013/roberta_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pawlo2013/roberta_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pawlo2013/roberta_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pawlo2013/roberta_sentiment") model = AutoModelForSequenceClassification.from_pretrained("pawlo2013/roberta_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 579f7ffbfd2bbe152fbf50d92e1e7e817ea5b038c723ce97447c1f7c56a443bb
- Size of remote file:
- 3.77 kB
- SHA256:
- a2fd90a1c31cfadb7518d3d2fa160f7a715d34d39e5c65e6c7825907d4ffad6f
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