Instructions to use frahman/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frahman/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="frahman/bert-base-uncased-issues-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("frahman/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("frahman/bert-base-uncased-issues-128") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e80886e24a6d18f11f24b8cfdba2877336df2c17111cd08e3fbdf3dd7a88097b
- Size of remote file:
- 2.99 kB
- SHA256:
- e22aaecf4c646fa0905ba2408aeade5ed4ca3309314c7d2f7b4a57ee32b7e64f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.