update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,81 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
metrics:
|
6 |
+
- accuracy
|
7 |
+
model-index:
|
8 |
+
- name: wav2vec2-base-finetuned-ks
|
9 |
+
results: []
|
10 |
+
---
|
11 |
+
|
12 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
13 |
+
should probably proofread and complete it, then remove this comment. -->
|
14 |
+
|
15 |
+
# wav2vec2-base-finetuned-ks
|
16 |
+
|
17 |
+
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
|
18 |
+
It achieves the following results on the evaluation set:
|
19 |
+
- Loss: 0.7101
|
20 |
+
- Accuracy: 0.7538
|
21 |
+
|
22 |
+
## Model description
|
23 |
+
|
24 |
+
More information needed
|
25 |
+
|
26 |
+
## Intended uses & limitations
|
27 |
+
|
28 |
+
More information needed
|
29 |
+
|
30 |
+
## Training and evaluation data
|
31 |
+
|
32 |
+
More information needed
|
33 |
+
|
34 |
+
## Training procedure
|
35 |
+
|
36 |
+
### Training hyperparameters
|
37 |
+
|
38 |
+
The following hyperparameters were used during training:
|
39 |
+
- learning_rate: 3e-05
|
40 |
+
- train_batch_size: 32
|
41 |
+
- eval_batch_size: 32
|
42 |
+
- seed: 42
|
43 |
+
- gradient_accumulation_steps: 4
|
44 |
+
- total_train_batch_size: 128
|
45 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
46 |
+
- lr_scheduler_type: linear
|
47 |
+
- lr_scheduler_warmup_ratio: 0.1
|
48 |
+
- num_epochs: 20
|
49 |
+
|
50 |
+
### Training results
|
51 |
+
|
52 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
53 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
54 |
+
| No log | 1.0 | 7 | 1.1448 | 0.5769 |
|
55 |
+
| 1.0433 | 2.0 | 14 | 1.0463 | 0.6077 |
|
56 |
+
| 0.9904 | 3.0 | 21 | 1.0912 | 0.5923 |
|
57 |
+
| 0.9904 | 4.0 | 28 | 1.0639 | 0.5769 |
|
58 |
+
| 0.8697 | 5.0 | 35 | 1.0283 | 0.6 |
|
59 |
+
| 0.7873 | 6.0 | 42 | 0.8870 | 0.7077 |
|
60 |
+
| 0.7873 | 7.0 | 49 | 0.8815 | 0.6538 |
|
61 |
+
| 0.7124 | 8.0 | 56 | 0.8828 | 0.6538 |
|
62 |
+
| 0.666 | 9.0 | 63 | 0.8701 | 0.6846 |
|
63 |
+
| 0.6376 | 10.0 | 70 | 0.8704 | 0.6692 |
|
64 |
+
| 0.6376 | 11.0 | 77 | 0.8934 | 0.7077 |
|
65 |
+
| 0.6552 | 12.0 | 84 | 0.8678 | 0.6692 |
|
66 |
+
| 0.5827 | 13.0 | 91 | 0.8471 | 0.7 |
|
67 |
+
| 0.5827 | 14.0 | 98 | 0.7986 | 0.7154 |
|
68 |
+
| 0.5557 | 15.0 | 105 | 0.7614 | 0.7462 |
|
69 |
+
| 0.5255 | 16.0 | 112 | 0.7847 | 0.7231 |
|
70 |
+
| 0.5255 | 17.0 | 119 | 0.7917 | 0.7154 |
|
71 |
+
| 0.5129 | 18.0 | 126 | 0.7101 | 0.7538 |
|
72 |
+
| 0.4621 | 19.0 | 133 | 0.7437 | 0.7385 |
|
73 |
+
| 0.4552 | 20.0 | 140 | 0.7404 | 0.7308 |
|
74 |
+
|
75 |
+
|
76 |
+
### Framework versions
|
77 |
+
|
78 |
+
- Transformers 4.11.3
|
79 |
+
- Pytorch 1.10.0+cu111
|
80 |
+
- Datasets 2.0.0
|
81 |
+
- Tokenizers 0.10.3
|