Instructions to use Mohamed-Emad/midcal_lambdaA1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mohamed-Emad/midcal_lambdaA1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Mohamed-Emad/midcal_lambdaA1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Mohamed-Emad/midcal_lambdaA1") model = AutoModelForCTC.from_pretrained("Mohamed-Emad/midcal_lambdaA1") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Mid Lambda EN
The model was trained on about 10,000 unique medical terms. You can see a summary of the data in the next section
Overview Dataset
__You can try pronunciation of words like shown in the table below
| Term | |
|---|---|
| 0 | indogesic |
| 1 | rothacin |
| 2 | indomethacin |
| 3 | methacin |
| 4 | indocid retard |
| 5 | flexin |
| 6 | elmetacin |
| 7 | ixifi |
| 8 | remsima |
| 9 | remicade |
| 10 | actacel vaccine |
| 11 | axitinib |
| 12 | isoprinosine |
| 13 | imunovir |
| 14 | icgreen |
| 15 | emacrit |
| 16 | emacrit capsules |
| 17 | sarclisa |
| 18 | cresemba |
| 19 | isepacine |
- Downloads last month
- 4