Instructions to use dfafdsaf/roberta_sentiment_5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dfafdsaf/roberta_sentiment_5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dfafdsaf/roberta_sentiment_5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dfafdsaf/roberta_sentiment_5000") model = AutoModelForSequenceClassification.from_pretrained("dfafdsaf/roberta_sentiment_5000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20f03b3d4d7505f5c7e11605fdf52c867545b5655e9d94795b9391531bfbee44
- Size of remote file:
- 499 MB
- SHA256:
- 2144db8604f0b7a47f4247fbd6964cb9501038137fe9aa8656e5db2dcb1b5a02
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