|
|
---
|
|
|
license: apache-2.0
|
|
|
base_model: facebook/hubert-base-ls960
|
|
|
tags:
|
|
|
- generated_from_trainer
|
|
|
metrics:
|
|
|
- accuracy
|
|
|
- precision
|
|
|
- recall
|
|
|
- f1
|
|
|
model-index:
|
|
|
- name: hubert-classifier
|
|
|
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. -->
|
|
|
|
|
|
# hubert-classifier
|
|
|
|
|
|
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
|
|
|
It achieves the following results on the evaluation set:
|
|
|
- Loss: 3.9330
|
|
|
- Accuracy: 0.0674
|
|
|
- Precision: 0.0116
|
|
|
- Recall: 0.0674
|
|
|
- F1: 0.0182
|
|
|
- Binary: 0.3423
|
|
|
|
|
|
## 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: 1e-05
|
|
|
- train_batch_size: 32
|
|
|
- eval_batch_size: 32
|
|
|
- seed: 42
|
|
|
- gradient_accumulation_steps: 4
|
|
|
- total_train_batch_size: 128
|
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
|
|
- lr_scheduler_type: linear
|
|
|
- num_epochs: 10
|
|
|
- mixed_precision_training: Native AMP
|
|
|
|
|
|
### Training results
|
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
|
|
|
| No log | 0.96 | 50 | 4.4099 | 0.0647 | 0.0191 | 0.0647 | 0.0221 | 0.2396 |
|
|
|
| No log | 1.91 | 100 | 4.3523 | 0.0593 | 0.0190 | 0.0593 | 0.0194 | 0.3019 |
|
|
|
| No log | 2.87 | 150 | 4.2416 | 0.0701 | 0.0246 | 0.0701 | 0.0235 | 0.3358 |
|
|
|
| No log | 3.83 | 200 | 4.1412 | 0.0701 | 0.0265 | 0.0701 | 0.0214 | 0.3437 |
|
|
|
| No log | 4.78 | 250 | 4.0716 | 0.0593 | 0.0069 | 0.0593 | 0.0122 | 0.3334 |
|
|
|
| No log | 5.74 | 300 | 4.0195 | 0.0701 | 0.0124 | 0.0701 | 0.0186 | 0.3453 |
|
|
|
| No log | 6.7 | 350 | 3.9850 | 0.0593 | 0.0073 | 0.0593 | 0.0126 | 0.3350 |
|
|
|
| No log | 7.66 | 400 | 3.9610 | 0.0647 | 0.0097 | 0.0647 | 0.0162 | 0.3388 |
|
|
|
| No log | 8.61 | 450 | 3.9420 | 0.0674 | 0.0113 | 0.0674 | 0.0180 | 0.3396 |
|
|
|
| 4.2019 | 9.57 | 500 | 3.9330 | 0.0674 | 0.0116 | 0.0674 | 0.0182 | 0.3423 |
|
|
|
|
|
|
|
|
|
### Framework versions
|
|
|
|
|
|
- Transformers 4.38.2
|
|
|
- Pytorch 2.3.0
|
|
|
- Datasets 2.19.1
|
|
|
- Tokenizers 0.15.1
|
|
|
|
