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
metadata
license: apache-2.0
base_model: darinchau/checkpoints
tags:
- generated_from_trainer
model-index:
- name: checkpoints
results: []
checkpoints
This model is a fine-tuned version of 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