Instructions to use mudes/en-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mudes/en-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mudes/en-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mudes/en-large") model = AutoModelForTokenClassification.from_pretrained("mudes/en-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mudes/en-large: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/mudes/en-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mudes/en-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mudes/en-large/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- e69489a763b7c195e2c3edd8781e19e7a36748d7db579423c8479cf43c15b61a
- Size of remote file:
- 1.42 GB
- SHA256:
- cfd9b244bead415eff53c72a5bb227dd153a15b0f13ba0e67e200d04daf8e8f6
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