Instructions to use Sjdan/mst_hp6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/mst_hp6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/mst_hp6")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/mst_hp6") model = AutoModelForCTC.from_pretrained("Sjdan/mst_hp6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sjdan/mst_hp6: direct link, hf CLI and curl.
- Browser
- Download file 1.58 kB
-
https://huggingface.co/Sjdan/mst_hp6/resolve/main/README.md
- Command line
-
hf download hf://Sjdan/mst_hp6/README.md
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curl -L -o README.md https://huggingface.co/Sjdan/mst_hp6/resolve/main/README.md
1.58 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mst_hp6
results: []
mst_hp6
This model is a fine-tuned version of Sjdan/mst_1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0166
- Wer: 1.7352
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.3198 | 1.36 | 500 | 0.4749 | 1.4891 |
| 0.5829 | 2.72 | 1000 | 0.1438 | 1.2368 |
| 0.2887 | 4.09 | 1500 | 0.0407 | 1.7788 |
| 0.1233 | 5.45 | 2000 | 0.0252 | 1.7508 |
| 0.0599 | 6.81 | 2500 | 0.0166 | 1.7352 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2