Instructions to use ggoggam/spanbert-base-cased-coref with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ggoggam/spanbert-base-cased-coref with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ggoggam/spanbert-base-cased-coref")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ggoggam/spanbert-base-cased-coref") model = AutoModel.from_pretrained("ggoggam/spanbert-base-cased-coref", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ggoggam/spanbert-base-cased-coref: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/ggoggam/spanbert-base-cased-coref/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ggoggam/spanbert-base-cased-coref/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ggoggam/spanbert-base-cased-coref/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- a4e45ee5f50f7eaa72d47748dd923c991e607b3b8561d6d791e00c949ef22562
- Size of remote file:
- 433 MB
- SHA256:
- f11f9c29b5c2c1618231be9738962352853421272e2df078382d36f36ab76782
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.