Instructions to use j-hartmann/MindMiner-Binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use j-hartmann/MindMiner-Binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="j-hartmann/MindMiner-Binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("j-hartmann/MindMiner-Binary") model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/MindMiner-Binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0a3667eede7ce5bc0930fbbb42e90a58913a351ff94d3a475ce2900adcc2c83
- Size of remote file:
- 501 MB
- SHA256:
- d49369a42b9a1d99ec98d6475a0f030d7b3d30d5dd6daa52897148942c4f04a1
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