Instructions to use darinchau/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darinchau/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="darinchau/checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("darinchau/checkpoints") model = AutoModelForSpeechSeq2Seq.from_pretrained("darinchau/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from darinchau/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/darinchau/checkpoints/resolve/main/README.md
- Command line
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hf download hf://darinchau/checkpoints/README.md
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curl -L -o README.md https://huggingface.co/darinchau/checkpoints/resolve/main/README.md
1.33 kB
| license: apache-2.0 | |
| base_model: darinchau/checkpoints | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: checkpoints | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # checkpoints | |
| This model is a fine-tuned version of [darinchau/checkpoints](https://huggingface.co/darinchau/checkpoints) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - eval_loss: 0.8289 | |
| - eval_cer: 79.9622 | |
| - eval_runtime: 31.6459 | |
| - eval_samples_per_second: 3.16 | |
| - eval_steps_per_second: 0.221 | |
| - epoch: 35.11 | |
| - step: 3300 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 4000 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |