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---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hubert-classifier-aug
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-aug
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: 4.4216
- Accuracy: 0.0148
- Precision: 0.0002
- Recall: 0.0148
- F1: 0.0004
- Binary: 0.1283
## 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: 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.15 | 50 | 4.4273 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1371 |
| No log | 0.31 | 100 | 4.4237 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1283 |
| No log | 0.46 | 150 | 4.4239 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1352 |
| No log | 0.62 | 200 | 4.4237 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1283 |
| No log | 0.77 | 250 | 4.4218 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1264 |
| No log | 0.92 | 300 | 4.4224 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1371 |
| No log | 1.08 | 350 | 4.4214 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1283 |
| No log | 1.23 | 400 | 4.4213 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1261 |
| No log | 1.39 | 450 | 4.4209 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1283 |
| 4.4252 | 1.54 | 500 | 4.4216 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1283 |
| 4.4252 | 1.69 | 550 | 4.4213 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1302 |
| 4.4252 | 1.85 | 600 | 4.4208 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1371 |
| 4.4252 | 2.0 | 650 | 4.4211 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1371 |
| 4.4252 | 2.16 | 700 | 4.4231 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1371 |
| 4.4252 | 2.31 | 750 | 4.4209 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1283 |
| 4.4252 | 2.47 | 800 | 4.4209 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1233 |
| 4.4252 | 2.62 | 850 | 4.4215 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1261 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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