Instructions to use rmacario/qxp-vs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmacario/qxp-vs2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rmacario/qxp-vs2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rmacario/qxp-vs2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download state.json from rmacario/qxp-vs2: direct link, hf CLI and curl.
- Browser
- Download file 362 Bytes
-
https://huggingface.co/rmacario/qxp-vs2/resolve/main/state.json
- Command line
-
hf download hf://rmacario/qxp-vs2/state.json
-
curl -L -o state.json https://huggingface.co/rmacario/qxp-vs2/resolve/main/state.json
362 Bytes
| { | |
| "vs2_best": { | |
| "epoch": 0, | |
| "scripted": { | |
| "cer": 3.0708702999577526, | |
| "wer": 19.436936936936938 | |
| }, | |
| "spontaneous": { | |
| "cer": 18.68953219258641, | |
| "wer": 36.193917494730506 | |
| }, | |
| "ood": { | |
| "cer": 6.122051082082277, | |
| "wer": 40.44943820224719 | |
| }, | |
| "mean": { | |
| "cer": 10.880201246272081, | |
| "wer": 27.815427215833722 | |
| } | |
| }, | |
| "vs2_epoch": 2 | |
| } |