File size: 2,717 Bytes
8884f5c |
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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- Emo-Codec/CREMA-D_synth
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilhubert-tone-classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: CREMA-D
type: Emo-Codec/CREMA-D_synth
metrics:
- name: Accuracy
type: accuracy
value: 0.6809651474530831
- name: Precision
type: precision
value: 0.6795129218164245
- name: Recall
type: recall
value: 0.6809651474530831
- name: F1
type: f1
value: 0.6750238551197275
---
<!-- 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. -->
# distilhubert-tone-classification
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the CREMA-D dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1796
- Accuracy: 0.6810
- Precision: 0.6795
- Recall: 0.6810
- F1: 0.6750
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3122 | 1.0 | 442 | 1.1656 | 0.5737 | 0.5887 | 0.5737 | 0.5679 |
| 1.0131 | 2.0 | 884 | 0.9625 | 0.6461 | 0.6572 | 0.6461 | 0.6399 |
| 0.7817 | 3.0 | 1326 | 1.0005 | 0.6381 | 0.6506 | 0.6381 | 0.6249 |
| 0.6087 | 4.0 | 1768 | 0.9428 | 0.6649 | 0.6572 | 0.6649 | 0.6515 |
| 0.4604 | 5.0 | 2210 | 1.0250 | 0.6622 | 0.6710 | 0.6622 | 0.6545 |
| 0.3164 | 6.0 | 2652 | 1.0814 | 0.6783 | 0.6821 | 0.6783 | 0.6656 |
| 0.2127 | 7.0 | 3094 | 1.1286 | 0.6971 | 0.6991 | 0.6971 | 0.6909 |
| 0.1224 | 8.0 | 3536 | 1.1796 | 0.6810 | 0.6795 | 0.6810 | 0.6750 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|