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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a52e2c1c90c915b44cf5a1e8f5629a2da31882f9d1ebdf7956a39a22b53db410
- Size of remote file:
- 1.42 GB
- SHA256:
- 42d262eb43f730e960dcc91cd6d034a5d4e056c103e21c52e0e1147545243167
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