Instructions to use Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD") model = AutoModelForSequenceClassification.from_pretrained("Yah216/Sentiment_Analysis_CAMelBERT_msa_sixteenth_HARD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c96312053df88f8a4594f3bedc57fd620f3f903e42eece767d7009b8af6b6221
- Size of remote file:
- 437 MB
- SHA256:
- 7567d6699d226aeb70d05b0a6380be5acc6dec2dd3eb7835263906efe19a3e57
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