Text Classification
Model2Vec
Safetensors
static-embeddings
newspaper-classification
crop-classification
historical-newspapers
newspapers
Instructions to use institutional/institutional-newspapers-crop-classifier-text-model2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use institutional/institutional-newspapers-crop-classifier-text-model2vec with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("institutional/institutional-newspapers-crop-classifier-text-model2vec") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download config.json from institutional/institutional-newspapers-crop-classifier-text-model2vec: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/institutional/institutional-newspapers-crop-classifier-text-model2vec/resolve/main/config.json
- Command line
-
hf download hf://institutional/institutional-newspapers-crop-classifier-text-model2vec/config.json
-
curl -L -o config.json https://huggingface.co/institutional/institutional-newspapers-crop-classifier-text-model2vec/resolve/main/config.json
97 Bytes
| { | |
| "normalize": true, | |
| "embedding_dtype": "float32", | |
| "vocabulary_quantization": 63091 | |
| } |