Instructions to use judithrosell/BERT_ST_DA_100_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/BERT_ST_DA_100_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/BERT_ST_DA_100_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/BERT_ST_DA_100_v2") model = AutoModelForTokenClassification.from_pretrained("judithrosell/BERT_ST_DA_100_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from judithrosell/BERT_ST_DA_100_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/judithrosell/BERT_ST_DA_100_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://judithrosell/BERT_ST_DA_100_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/judithrosell/BERT_ST_DA_100_v2/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- d72fb8aea7cc3652ca5b72e96ac1c7fc49bc7bc124b87b2c2fbd01b2bff733b2
- Size of remote file:
- 5.18 kB
- SHA256:
- 7963c945833e6533c6da05c16e76edb4807503e6a3329bfe7ca2c6a334dad2c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.