Instructions to use faycadnz/IMFBERT_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faycadnz/IMFBERT_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faycadnz/IMFBERT_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("faycadnz/IMFBERT_binary") model = AutoModelForSequenceClassification.from_pretrained("faycadnz/IMFBERT_binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42f2a8277141e11395eba124f5dcda3ccf4dee9a6cc69c3fd1431b39c66b5f0e
- Size of remote file:
- 1.42 GB
- SHA256:
- fe7904435cb8173c36ce61d8f0d8d9b4745a9f6ea766d9c7d3133df2d3a52bf3
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