Instructions to use robinhad/data2vec-large-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robinhad/data2vec-large-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="robinhad/data2vec-large-uk")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("robinhad/data2vec-large-uk") model = AutoModelForCTC.from_pretrained("robinhad/data2vec-large-uk") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 914c0f04d4f3920900fa84c6772f5005d6304022652003e99ae4b9d78d10a2ea
- Size of remote file:
- 559 Bytes
- SHA256:
- b980b119c19da5f512c2e24a5338956f1410654c202fc467cdb70307305a2488
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.