---
license: apache-2.0
base_model: facebook/hubert-large-ls960-ft
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mascir_fr_hubert_version1000
  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. -->

# mascir_fr_hubert_version1000

This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co./facebook/hubert-large-ls960-ft) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0469
- Wer: 0.5322

## 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: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log        | 2.0   | 250   | 3.0909          | 1.0    |
| 5.0589        | 4.0   | 500   | 2.9060          | 1.0    |
| 5.0589        | 6.0   | 750   | 1.3938          | 0.9789 |
| 1.8801        | 8.0   | 1000  | 0.9636          | 0.8422 |
| 1.8801        | 10.0  | 1250  | 0.8361          | 0.7644 |
| 0.8006        | 12.0  | 1500  | 0.8474          | 0.7444 |
| 0.8006        | 14.0  | 1750  | 0.8360          | 0.7078 |
| 0.5307        | 16.0  | 2000  | 0.8514          | 0.6944 |
| 0.5307        | 18.0  | 2250  | 0.8770          | 0.6544 |
| 0.3998        | 20.0  | 2500  | 0.8200          | 0.65   |
| 0.3998        | 22.0  | 2750  | 0.8362          | 0.63   |
| 0.3281        | 24.0  | 3000  | 0.8933          | 0.6144 |
| 0.3281        | 26.0  | 3250  | 0.9355          | 0.62   |
| 0.262         | 28.0  | 3500  | 0.9134          | 0.6222 |
| 0.262         | 30.0  | 3750  | 0.9302          | 0.5989 |
| 0.2256        | 32.0  | 4000  | 0.9307          | 0.5856 |
| 0.2256        | 34.0  | 4250  | 0.9078          | 0.6011 |
| 0.2011        | 36.0  | 4500  | 0.9647          | 0.5822 |
| 0.2011        | 38.0  | 4750  | 0.9252          | 0.5844 |
| 0.1819        | 40.0  | 5000  | 0.9917          | 0.5711 |
| 0.1819        | 42.0  | 5250  | 0.9577          | 0.5678 |
| 0.1706        | 44.0  | 5500  | 1.0094          | 0.5722 |
| 0.1706        | 46.0  | 5750  | 0.9774          | 0.5722 |
| 0.1504        | 48.0  | 6000  | 0.9702          | 0.5456 |
| 0.1504        | 50.0  | 6250  | 0.9575          | 0.5756 |
| 0.1509        | 52.0  | 6500  | 0.9855          | 0.5644 |
| 0.1509        | 54.0  | 6750  | 0.9429          | 0.5411 |
| 0.1292        | 56.0  | 7000  | 1.0471          | 0.5644 |
| 0.1292        | 58.0  | 7250  | 1.0106          | 0.5589 |
| 0.1217        | 60.0  | 7500  | 1.0118          | 0.5544 |
| 0.1217        | 62.0  | 7750  | 1.0415          | 0.5478 |
| 0.1187        | 64.0  | 8000  | 1.0047          | 0.5489 |
| 0.1187        | 66.0  | 8250  | 1.0700          | 0.5644 |
| 0.1075        | 68.0  | 8500  | 1.0357          | 0.5444 |
| 0.1075        | 70.0  | 8750  | 0.9647          | 0.5444 |
| 0.1009        | 72.0  | 9000  | 1.0392          | 0.5489 |
| 0.1009        | 74.0  | 9250  | 1.0569          | 0.5433 |
| 0.0997        | 76.0  | 9500  | 1.0266          | 0.5456 |
| 0.0997        | 78.0  | 9750  | 1.0328          | 0.54   |
| 0.101         | 80.0  | 10000 | 1.0338          | 0.5522 |
| 0.101         | 82.0  | 10250 | 1.0422          | 0.5511 |
| 0.088         | 84.0  | 10500 | 1.0233          | 0.55   |
| 0.088         | 86.0  | 10750 | 1.0446          | 0.5522 |
| 0.0922        | 88.0  | 11000 | 1.0558          | 0.5467 |
| 0.0922        | 90.0  | 11250 | 1.0405          | 0.5433 |
| 0.0863        | 92.0  | 11500 | 1.0336          | 0.5322 |
| 0.0863        | 94.0  | 11750 | 1.0575          | 0.5356 |
| 0.0845        | 96.0  | 12000 | 1.0449          | 0.5378 |
| 0.0845        | 98.0  | 12250 | 1.0482          | 0.5344 |
| 0.0818        | 100.0 | 12500 | 1.0469          | 0.5322 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3