Instructions to use EzekielMW/pazarf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EzekielMW/pazarf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EzekielMW/pazarf", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EzekielMW/pazarf") model = AutoModelForSpeechSeq2Seq.from_pretrained("EzekielMW/pazarf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|endoftext|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "<|luo|>" | |
| ], | |
| "is_local": false, | |
| "language": null, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "model_specific_special_tokens": {}, | |
| "pad_token": "<|endoftext|>", | |
| "predict_timestamps": false, | |
| "processor_class": "WhisperProcessor", | |
| "task": null, | |
| "tokenizer_class": "WhisperTokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |