Instructions to use colorlessideas/mms-1bl1107-ulwa-60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use colorlessideas/mms-1bl1107-ulwa-60 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="colorlessideas/mms-1bl1107-ulwa-60")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("colorlessideas/mms-1bl1107-ulwa-60") model = AutoModelForCTC.from_pretrained("colorlessideas/mms-1bl1107-ulwa-60") - Notebooks
- Google Colab
- Kaggle
mms-1bl1107-ulwa-60
This model is a fine-tuned version of facebook/mms-1b-l1107 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3150
- Cer: 0.0900
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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 | Cer |
|---|---|---|---|---|
| 2.618 | 12.9180 | 400 | 0.6538 | 0.1731 |
| 0.7453 | 25.8197 | 800 | 0.3150 | 0.0900 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for colorlessideas/mms-1bl1107-ulwa-60
Base model
facebook/mms-1b-l1107