Instructions to use APJ23/MultiHeaded_Sentiment_Analysis_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use APJ23/MultiHeaded_Sentiment_Analysis_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="APJ23/MultiHeaded_Sentiment_Analysis_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model") model = AutoModelForSequenceClassification.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a3fbf606122fd613ea00cc977c22015a77390653d3644e34c88139fc502a9a6
- Size of remote file:
- 433 MB
- SHA256:
- 6c222f234f638851384048786d9b35b271a9a90c2dbc99821ea617f9a46c7656
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