Instructions to use cdactvm/w2vbert-punjabi-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cdactvm/w2vbert-punjabi-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cdactvm/w2vbert-punjabi-quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cdactvm/w2vbert-punjabi-quantized") model = AutoModelForCTC.from_pretrained("cdactvm/w2vbert-punjabi-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from cdactvm/w2vbert-punjabi-quantized: direct link, hf CLI and curl.
- Browser
- Download file 96 Bytes
-
https://huggingface.co/cdactvm/w2vbert-punjabi-quantized/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://cdactvm/w2vbert-punjabi-quantized/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/cdactvm/w2vbert-punjabi-quantized/resolve/main/special_tokens_map.json
96 Bytes
| { | |
| "bos_token": "<s>", | |
| "eos_token": "</s>", | |
| "pad_token": "[PAD]", | |
| "unk_token": "[UNK]" | |
| } | |