Instructions to use emilys/hmBERT-CoNLL-cp3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilys/hmBERT-CoNLL-cp3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="emilys/hmBERT-CoNLL-cp3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("emilys/hmBERT-CoNLL-cp3") model = AutoModelForTokenClassification.from_pretrained("emilys/hmBERT-CoNLL-cp3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from emilys/hmBERT-CoNLL-cp3: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/emilys/hmBERT-CoNLL-cp3/resolve/main/training_args.bin
- Command line
-
hf download hf://emilys/hmBERT-CoNLL-cp3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/emilys/hmBERT-CoNLL-cp3/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- c5297eaf51e425c6789f9893066130980877700f5dc70a93dc93a0cd7e060d1d
- Size of remote file:
- 3.25 kB
- SHA256:
- e1b71fed2f689284142bb334fb346d008b29ae77086b74513c480771ef6e3e5d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.