Instructions to use vilsonrodrigues/bert_cas_model_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vilsonrodrigues/bert_cas_model_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vilsonrodrigues/bert_cas_model_beta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vilsonrodrigues/bert_cas_model_beta") model = AutoModelForTokenClassification.from_pretrained("vilsonrodrigues/bert_cas_model_beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vilsonrodrigues/bert_cas_model_beta: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/vilsonrodrigues/bert_cas_model_beta/resolve/main/training_args.bin
- Command line
-
hf download hf://vilsonrodrigues/bert_cas_model_beta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vilsonrodrigues/bert_cas_model_beta/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- 3cc3ff099017648019e30bc44c001b0d0619122b79e3d9692019572497a3baa9
- Size of remote file:
- 4.86 kB
- SHA256:
- b2852cfb446f85a04c1e104235e2672826778d5330f0fb28bf7b0cb8092e0c79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.