Instructions to use Bedru/dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bedru/dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Bedru/dir")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Bedru/dir") model = AutoModelForSpeechSeq2Seq.from_pretrained("Bedru/dir") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 922a1b7a0f6a947a115b8b11906638f02858282604ce759ceb94f9d540b73fa0
- Size of remote file:
- 5.33 kB
- SHA256:
- 79927b1e3359f27565f417016afe7a01cc9b9736f5967de9f4791e976c3dcf63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.