Instructions to use Wzf/bert_fintuuing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wzf/bert_fintuuing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Wzf/bert_fintuuing")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Wzf/bert_fintuuing") model = AutoModelForMaskedLM.from_pretrained("Wzf/bert_fintuuing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Wzf/bert_fintuuing: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/Wzf/bert_fintuuing/resolve/main/training_args.bin
- Command line
-
hf download hf://Wzf/bert_fintuuing/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Wzf/bert_fintuuing/resolve/main/training_args.bin
1.21 kB
- Xet hash:
- bb4ebd575888d0da6678b28fc44c40415451b78472ba0e8fbb1e9418b95c4ede
- Size of remote file:
- 1.21 kB
- SHA256:
- 4605b543d2ab94bbe2dce69f9fa9a4b575a3b2d25f1bd8fb935c14571b0da6cd
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