Instructions to use yacoubagbane/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yacoubagbane/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yacoubagbane/checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("yacoubagbane/checkpoints") model = AutoModelForCTC.from_pretrained("yacoubagbane/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from yacoubagbane/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 1.81 kB
-
https://huggingface.co/yacoubagbane/checkpoints/resolve/main/README.md
- Command line
-
hf download hf://yacoubagbane/checkpoints/README.md
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curl -L -o README.md https://huggingface.co/yacoubagbane/checkpoints/resolve/main/README.md
1.81 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: checkpoints
results: []
checkpoints
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1084
- Wer: 1.0
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.4567 | 0.7962 | 500 | 3.1191 | 1.0 |
| 3.0371 | 1.5924 | 1000 | 3.1471 | 1.0 |
| 3.0352 | 2.3885 | 1500 | 3.1423 | 1.0 |
| 3.0396 | 3.1847 | 2000 | 3.1056 | 1.0 |
| 3.0361 | 3.9809 | 2500 | 3.1074 | 1.0 |
| 3.0391 | 4.7771 | 3000 | 3.1084 | 1.0 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2