Instructions to use Zenith754/goodreads-bert-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zenith754/goodreads-bert-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zenith754/goodreads-bert-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zenith754/goodreads-bert-classifier") model = AutoModelForSequenceClassification.from_pretrained("Zenith754/goodreads-bert-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-250/optimizer.pt from Zenith754/goodreads-bert-classifier: direct link, hf CLI and curl.
- Browser
- Download file 526 MB
-
https://huggingface.co/Zenith754/goodreads-bert-classifier/resolve/main/checkpoint-250/optimizer.pt
- Command line
-
hf download hf://Zenith754/goodreads-bert-classifier/checkpoint-250/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Zenith754/goodreads-bert-classifier/resolve/main/checkpoint-250/optimizer.pt
526 MB
- Xet hash:
- 5e0de31e8f4dccb1af5479994857b45b2db24cba124e5208e4be23d33013a86c
- Size of remote file:
- 526 MB
- SHA256:
- bd531136e2358ddcc74a9fa9d63caff659f0f8b74c467a14ddbfd860c5ad1161
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.