Instructions to use scasutt/Prototype_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/Prototype_training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/Prototype_training")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/Prototype_training") model = AutoModelForCTC.from_pretrained("scasutt/Prototype_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from scasutt/Prototype_training: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/scasutt/Prototype_training/resolve/refs%2Fpr%2F1/README.md
- Command line
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hf download hf://scasutt/Prototype_training@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/scasutt/Prototype_training/resolve/refs%2Fpr%2F1/README.md
1.47 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Prototype_training
results: []
Prototype_training
This model is a fine-tuned version of scasutt/Prototype_training on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3719
- Wer: 0.4626
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3853 | 1.47 | 100 | 0.3719 | 0.4626 |
| 0.3867 | 2.94 | 200 | 0.3719 | 0.4626 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3