|
---
|
|
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: 2.6644
|
|
- Accuracy: 0.4576
|
|
- Precision: 0.4384
|
|
- Recall: 0.4576
|
|
- F1: 0.3988
|
|
- Binary: 0.6165
|
|
|
|
## 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: 5e-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 | 0.86 | 50 | 4.1527 | 0.0484 | 0.0321 | 0.0484 | 0.0218 | 0.3266 |
|
|
| No log | 1.72 | 100 | 3.7623 | 0.0605 | 0.0356 | 0.0605 | 0.0253 | 0.3361 |
|
|
| No log | 2.59 | 150 | 3.5557 | 0.1211 | 0.1031 | 0.1211 | 0.0914 | 0.3731 |
|
|
| No log | 3.45 | 200 | 3.3630 | 0.2131 | 0.1912 | 0.2131 | 0.1632 | 0.4375 |
|
|
| No log | 4.31 | 250 | 3.1590 | 0.2809 | 0.2107 | 0.2809 | 0.2096 | 0.4915 |
|
|
| No log | 5.17 | 300 | 3.0569 | 0.3414 | 0.3012 | 0.3414 | 0.2825 | 0.5312 |
|
|
| No log | 6.03 | 350 | 2.8798 | 0.3995 | 0.3279 | 0.3995 | 0.3298 | 0.5751 |
|
|
| No log | 6.9 | 400 | 2.7761 | 0.4189 | 0.3461 | 0.4189 | 0.3522 | 0.5918 |
|
|
| No log | 7.76 | 450 | 2.7086 | 0.4383 | 0.3693 | 0.4383 | 0.3689 | 0.6036 |
|
|
| 3.4503 | 8.62 | 500 | 2.6644 | 0.4576 | 0.4384 | 0.4576 | 0.3988 | 0.6165 |
|
|
| 3.4503 | 9.48 | 550 | 2.6426 | 0.4649 | 0.4400 | 0.4649 | 0.4021 | 0.6211 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.38.2
|
|
- Pytorch 2.3.0
|
|
- Datasets 2.19.1
|
|
- Tokenizers 0.15.1
|
|
|
