Instructions to use philschmid/finbert-pretrain-yiyanghkust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/finbert-pretrain-yiyanghkust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="philschmid/finbert-pretrain-yiyanghkust")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("philschmid/finbert-pretrain-yiyanghkust") model = AutoModelForMaskedLM.from_pretrained("philschmid/finbert-pretrain-yiyanghkust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from philschmid/finbert-pretrain-yiyanghkust: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/philschmid/finbert-pretrain-yiyanghkust/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://philschmid/finbert-pretrain-yiyanghkust/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/philschmid/finbert-pretrain-yiyanghkust/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- e9861116d765b4da0af8596196064a18b0ffd317151a7518c03f4f8b331c0631
- Size of remote file:
- 439 MB
- SHA256:
- 717e2462c824deab28fede4b51c5a59cf2f8e3758bcbf0a1841a00f52d7014a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.