Instructions to use hf-internal-testing/tiny-random-ConvBertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvBertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-ConvBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ConvBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-ConvBertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa115d50417b47c74eaf0e1047de47ca031d1b35cd324c3e48c0de9496883736
- Size of remote file:
- 5.45 MB
- SHA256:
- 053fe3983accb63f41f5c8b2cbe2120967da55e24d2e2150935994cb54d72ae5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.