Instructions to use Politus/swa_eng_org_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Politus/swa_eng_org_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Politus/swa_eng_org_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Politus/swa_eng_org_model") model = AutoModelForSequenceClassification.from_pretrained("Politus/swa_eng_org_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Politus/swa_eng_org_model: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Politus/swa_eng_org_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Politus/swa_eng_org_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Politus/swa_eng_org_model/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 402d8552798a710a24199e3069d0528ea8b231508c2fca588042e38dd3666a08
- Size of remote file:
- 17.1 MB
- SHA256:
- 16d37d004fc8ae3247e28962cb6162818421903ed6491755cb287c23bcfd4b87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.