File size: 1,571 Bytes
702a4b6
625c755
5b25247
ae7935e
 
5b25247
 
625c755
 
 
702a4b6
 
625c755
 
702a4b6
625c755
 
702a4b6
ae7935e
 
 
 
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
702a4b6
625c755
 
 
 
 
 
 
 
 
 
 
 
702a4b6
ae7935e
 
 
 
625c755
702a4b6
625c755
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
---
base_model: facebook/wav2vec2-xls-r-300m
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: xls_1b_decoding_fr_decoding_test
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/danakal/xls_300m_french_data/runs/cf8f5rsv)
# xls_1b_decoding_fr_decoding_test

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 14.6705
- 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1