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:
- b5e99b7387d2c3688b895d5c48b7680ba994a4f6d12897361ece481015a7ac4b
- Size of remote file:
- 1.42 GB
- SHA256:
- f3f79cefb6c91b9caec071b3d9f04dbc40ba91494ce29799f682ab80aba8c878
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.