Instructions to use mudes/en-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mudes/en-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mudes/en-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mudes/en-base") model = AutoModelForTokenClassification.from_pretrained("mudes/en-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mudes/en-base: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/mudes/en-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mudes/en-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mudes/en-base/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- 844153b9d743e8bcbc8d3839373380ad00040c02e1ae24a4c8d04a2d3cfaab3b
- Size of remote file:
- 431 MB
- SHA256:
- dc57e1321e5d28c29faa82cff255b746816d341a5d477097aa0a0684a4511659
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