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# wav2vec2-base-finetuned-sentiment-mesd-v9

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3500
- Accuracy: 0.9154

## 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: 64
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.86  | 3    | 1.7825          | 0.1846   |
| 1.9553        | 1.86  | 6    | 1.7212          | 0.4308   |
| 1.9553        | 2.86  | 9    | 1.6164          | 0.3769   |
| 2.002         | 3.86  | 12   | 1.4904          | 0.3769   |
| 1.6191        | 4.86  | 15   | 1.4426          | 0.4385   |
| 1.6191        | 5.86  | 18   | 1.3516          | 0.5231   |
| 1.6209        | 6.86  | 21   | 1.2176          | 0.5538   |
| 1.6209        | 7.86  | 24   | 1.1683          | 0.5692   |
| 1.371         | 8.86  | 27   | 1.0885          | 0.5923   |
| 1.1568        | 9.86  | 30   | 1.0152          | 0.6385   |
| 1.1568        | 10.86 | 33   | 0.9289          | 0.6385   |
| 1.1023        | 11.86 | 36   | 0.9141          | 0.6308   |
| 1.1023        | 12.86 | 39   | 0.8526          | 0.6462   |
| 0.9448        | 13.86 | 42   | 0.8420          | 0.6769   |
| 0.7972        | 14.86 | 45   | 0.7976          | 0.6692   |
| 0.7972        | 15.86 | 48   | 0.8192          | 0.7308   |
| 0.7793        | 16.86 | 51   | 0.7108          | 0.7615   |
| 0.7793        | 17.86 | 54   | 0.6712          | 0.7769   |
| 0.6468        | 18.86 | 57   | 0.6684          | 0.7923   |
| 0.5083        | 19.86 | 60   | 0.6922          | 0.7385   |
| 0.5083        | 20.86 | 63   | 0.6148          | 0.7923   |
| 0.4988        | 21.86 | 66   | 0.5846          | 0.7923   |
| 0.4988        | 22.86 | 69   | 0.6050          | 0.8154   |
| 0.4123        | 23.86 | 72   | 0.5506          | 0.7846   |
| 0.3511        | 24.86 | 75   | 0.6095          | 0.7846   |
| 0.3511        | 25.86 | 78   | 0.5916          | 0.8154   |
| 0.3268        | 26.86 | 81   | 0.5912          | 0.8077   |
| 0.3268        | 27.86 | 84   | 0.5142          | 0.8538   |
| 0.3036        | 28.86 | 87   | 0.5492          | 0.8077   |
| 0.3066        | 29.86 | 90   | 0.6007          | 0.8231   |
| 0.3066        | 30.86 | 93   | 0.5748          | 0.8231   |
| 0.2538        | 31.86 | 96   | 0.6027          | 0.7692   |
| 0.2538        | 32.86 | 99   | 0.6979          | 0.7462   |
| 0.2281        | 33.86 | 102  | 0.7002          | 0.7615   |
| 0.2183        | 34.86 | 105  | 0.6650          | 0.7769   |
| 0.2183        | 35.86 | 108  | 0.5192          | 0.8462   |
| 0.2202        | 36.86 | 111  | 0.5389          | 0.8308   |
| 0.2202        | 37.86 | 114  | 0.5050          | 0.8385   |
| 0.1906        | 38.86 | 117  | 0.5722          | 0.7769   |
| 0.154         | 39.86 | 120  | 0.5239          | 0.8308   |
| 0.154         | 40.86 | 123  | 0.4448          | 0.8615   |
| 0.1474        | 41.86 | 126  | 0.4623          | 0.8615   |
| 0.1474        | 42.86 | 129  | 0.4282          | 0.8615   |
| 0.1345        | 43.86 | 132  | 0.5087          | 0.8615   |
| 0.1567        | 44.86 | 135  | 0.4859          | 0.8385   |
| 0.1567        | 45.86 | 138  | 0.6603          | 0.8077   |
| 0.1731        | 46.86 | 141  | 0.5379          | 0.8385   |
| 0.1731        | 47.86 | 144  | 0.8666          | 0.7538   |
| 0.1606        | 48.86 | 147  | 0.7518          | 0.8      |
| 0.1484        | 49.86 | 150  | 0.5986          | 0.8385   |
| 0.1484        | 50.86 | 153  | 0.6368          | 0.8231   |
| 0.2256        | 51.86 | 156  | 0.4639          | 0.8692   |
| 0.2256        | 52.86 | 159  | 0.5533          | 0.8462   |
| 0.1178        | 53.86 | 162  | 0.5038          | 0.8615   |
| 0.0815        | 54.86 | 165  | 0.5052          | 0.8692   |
| 0.0815        | 55.86 | 168  | 0.4337          | 0.8846   |
| 0.0998        | 56.86 | 171  | 0.4422          | 0.8769   |
| 0.0998        | 57.86 | 174  | 0.4317          | 0.8692   |
| 0.0855        | 58.86 | 177  | 0.4025          | 0.8923   |
| 0.0962        | 59.86 | 180  | 0.4605          | 0.8769   |
| 0.0962        | 60.86 | 183  | 0.4356          | 0.8769   |
| 0.0763        | 61.86 | 186  | 0.4614          | 0.8769   |
| 0.0763        | 62.86 | 189  | 0.4382          | 0.8846   |
| 0.0902        | 63.86 | 192  | 0.4701          | 0.8692   |
| 0.0654        | 64.86 | 195  | 0.4922          | 0.8692   |
| 0.0654        | 65.86 | 198  | 0.5413          | 0.8538   |
| 0.0651        | 66.86 | 201  | 0.5759          | 0.8615   |
| 0.0651        | 67.86 | 204  | 0.4238          | 0.9      |
| 0.0822        | 68.86 | 207  | 0.3500          | 0.9154   |
| 0.0625        | 69.86 | 210  | 0.3878          | 0.8923   |
| 0.0625        | 70.86 | 213  | 0.4952          | 0.8615   |
| 0.0548        | 71.86 | 216  | 0.4544          | 0.8615   |
| 0.0548        | 72.86 | 219  | 0.5497          | 0.8769   |
| 0.054         | 73.86 | 222  | 0.4434          | 0.8846   |
| 0.0543        | 74.86 | 225  | 0.4732          | 0.8769   |
| 0.0543        | 75.86 | 228  | 0.4425          | 0.8923   |
| 0.0881        | 76.86 | 231  | 0.4788          | 0.8769   |
| 0.0881        | 77.86 | 234  | 0.5448          | 0.8769   |
| 0.061         | 78.86 | 237  | 0.4221          | 0.9077   |
| 0.0567        | 79.86 | 240  | 0.4404          | 0.8769   |
| 0.0567        | 80.86 | 243  | 0.4099          | 0.9      |
| 0.052         | 81.86 | 246  | 0.5259          | 0.8769   |
| 0.052         | 82.86 | 249  | 0.5874          | 0.8692   |
| 0.0444        | 83.86 | 252  | 0.5555          | 0.8846   |
| 0.0332        | 84.86 | 255  | 0.5156          | 0.8615   |
| 0.0332        | 85.86 | 258  | 0.4564          | 0.8615   |
| 0.0449        | 86.86 | 261  | 0.4826          | 0.8692   |
| 0.0449        | 87.86 | 264  | 0.4726          | 0.8615   |
| 0.0385        | 88.86 | 267  | 0.4206          | 0.8846   |
| 0.0356        | 89.86 | 270  | 0.4050          | 0.8769   |
| 0.0356        | 90.86 | 273  | 0.4161          | 0.8923   |
| 0.0391        | 91.86 | 276  | 0.4100          | 0.9077   |
| 0.0391        | 92.86 | 279  | 0.4047          | 0.9      |
| 0.0249        | 93.86 | 282  | 0.4044          | 0.9      |
| 0.0399        | 94.86 | 285  | 0.3968          | 0.8846   |
| 0.0399        | 95.86 | 288  | 0.3802          | 0.9      |
| 0.031         | 96.86 | 291  | 0.3689          | 0.9      |
| 0.031         | 97.86 | 294  | 0.3616          | 0.9077   |
| 0.036         | 98.86 | 297  | 0.3584          | 0.9077   |
| 0.0386        | 99.86 | 300  | 0.3574          | 0.9077   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-sentiment-mesd-v9", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-finetuned-sentiment-mesd-v9
=========================================


This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3500
* Accuracy: 0.9154


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: 64
* eval\_batch\_size: 40
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 40
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 40
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 40
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6