|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
model-index: |
|
- name: xtreme_s_xlsr_minds14 |
|
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. --> |
|
|
|
# xtreme_s_xlsr_minds14 |
|
|
|
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: 0.2566 |
|
- F1: {'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471} |
|
|
|
## 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: 0.0003 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- total_train_batch_size: 64 |
|
- total_eval_batch_size: 16 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 1500 |
|
- num_epochs: 50.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:| |
|
| 2.551 | 2.7 | 200 | 2.5921 | {'f1': 0.03454307545755678, 'accuracy': 0.1148442272449603} | |
|
| 1.6934 | 5.41 | 400 | 1.5353 | {'f1': 0.5831241711045994, 'accuracy': 0.6053756872327428} | |
|
| 0.5914 | 8.11 | 600 | 0.7337 | {'f1': 0.7990425247664236, 'accuracy': 0.7947464874770922} | |
|
| 0.3896 | 10.81 | 800 | 0.5076 | {'f1': 0.8738199236080776, 'accuracy': 0.872327428222358} | |
|
| 0.5052 | 13.51 | 1000 | 0.4917 | {'f1': 0.8744760456867134, 'accuracy': 0.8747709224190593} | |
|
| 0.4806 | 16.22 | 1200 | 0.4751 | {'f1': 0.8840798740258787, 'accuracy': 0.8845448992058644} | |
|
| 0.2103 | 18.92 | 1400 | 0.5228 | {'f1': 0.8721632556623751, 'accuracy': 0.8729383017715333} | |
|
| 0.4198 | 21.62 | 1600 | 0.5910 | {'f1': 0.8755207264572983, 'accuracy': 0.8766035430665852} | |
|
| 0.11 | 24.32 | 1800 | 0.4464 | {'f1': 0.896423086249818, 'accuracy': 0.8955406230910201} | |
|
| 0.1233 | 27.03 | 2000 | 0.3760 | {'f1': 0.9012283567348968, 'accuracy': 0.9016493585827734} | |
|
| 0.1827 | 29.73 | 2200 | 0.4178 | {'f1': 0.9042381720184095, 'accuracy': 0.9059254734270006} | |
|
| 0.1235 | 32.43 | 2400 | 0.4152 | {'f1': 0.9063257163259107, 'accuracy': 0.9071472205253512} | |
|
| 0.1873 | 35.14 | 2600 | 0.2903 | {'f1': 0.9369340598806323, 'accuracy': 0.9376908979841173} | |
|
| 0.017 | 37.84 | 2800 | 0.3046 | {'f1': 0.9300781160576355, 'accuracy': 0.9303604153940135} | |
|
| 0.0436 | 40.54 | 3000 | 0.3111 | {'f1': 0.9315034391389341, 'accuracy': 0.9321930360415394} | |
|
| 0.0455 | 43.24 | 3200 | 0.2748 | {'f1': 0.9417365311433034, 'accuracy': 0.9425778863775198} | |
|
| 0.046 | 45.95 | 3400 | 0.2800 | {'f1': 0.9390712658440112, 'accuracy': 0.9395235186316433} | |
|
| 0.0042 | 48.65 | 3600 | 0.2566 | {'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471} | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.18.0.dev0 |
|
- Pytorch 1.10.2+cu113 |
|
- Datasets 1.18.4.dev0 |
|
- Tokenizers 0.11.6 |
|
|
