Instructions to use anablasi/financial_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anablasi/financial_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="anablasi/financial_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("anablasi/financial_model") model = AutoModelForMaskedLM.from_pretrained("anablasi/financial_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from anablasi/financial_model: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/anablasi/financial_model/resolve/main/training_args.bin
- Command line
-
hf download hf://anablasi/financial_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/anablasi/financial_model/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 315234e93e69e9882f3161937c541673b97db66f769ac2fa2e8b086e9c776268
- Size of remote file:
- 3.12 kB
- SHA256:
- 6357745991ea2164042b19fe7fedc623b32d8c4f7e42f593fc554a20b87202d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.