Instructions to use MatricariaV/Kabardian-ASR-kaggle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatricariaV/Kabardian-ASR-kaggle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MatricariaV/Kabardian-ASR-kaggle")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("MatricariaV/Kabardian-ASR-kaggle") model = AutoModelForCTC.from_pretrained("MatricariaV/Kabardian-ASR-kaggle", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,271 Bytes
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library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Kabardian-ASR-kaggle
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Kabardian-ASR-kaggle
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1748
- Wer: 0.3268
## 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: 8
- eval_batch_size: 8
- seed: 42
- 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: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.6132 | 0.3040 | 200 | 0.4195 | 0.6846 |
| 0.4931 | 0.6079 | 400 | 0.3299 | 0.5413 |
| 0.3934 | 0.9119 | 600 | 0.2873 | 0.5070 |
| 0.3771 | 1.2158 | 800 | 0.2497 | 0.4569 |
| 0.3445 | 1.5198 | 1000 | 0.2516 | 0.4304 |
| 0.3724 | 1.8237 | 1200 | 0.2439 | 0.4079 |
| 0.3265 | 2.1277 | 1400 | 0.2083 | 0.4065 |
| 0.2983 | 2.4316 | 1600 | 0.2082 | 0.3693 |
| 0.3222 | 2.7356 | 1800 | 0.2073 | 0.3688 |
| 0.3461 | 3.0395 | 2000 | 0.1854 | 0.3438 |
| 0.2745 | 3.3435 | 2200 | 0.1813 | 0.3329 |
| 0.2867 | 3.6474 | 2400 | 0.1784 | 0.3258 |
| 0.2715 | 3.9514 | 2600 | 0.1748 | 0.3268 |
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0
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