Instructions to use appier-rey/reader-600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appier-rey/reader-600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="appier-rey/reader-600")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("appier-rey/reader-600") model = AutoModelForTokenClassification.from_pretrained("appier-rey/reader-600") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11a16e4737680a1b896ce54b49393fe1c89e86e2d04d878f2b91ac0f100ba974
- Size of remote file:
- 11.4 MB
- SHA256:
- 8bc2f8f47d5b522701b4b644d1ffb3c261696eb469f1c31230b7305379ed60cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.