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# xtreme_s_w2v2_minds14.en-US

This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5337
- F1: 0.9144
- Accuracy: 0.9113

## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 150.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|
| 2.6482        | 3.95   | 20   | 2.6421          | 0.0242 | 0.0745   |
| 2.6292        | 7.95   | 40   | 2.6359          | 0.0108 | 0.0816   |
| 2.5993        | 11.95  | 60   | 2.6301          | 0.0167 | 0.0674   |
| 2.4023        | 15.95  | 80   | 2.5514          | 0.1105 | 0.1454   |
| 1.4015        | 19.95  | 100  | 1.6843          | 0.5599 | 0.5851   |
| 0.4379        | 23.95  | 120  | 0.8126          | 0.7921 | 0.7908   |
| 0.0642        | 27.95  | 140  | 0.7178          | 0.8158 | 0.8156   |
| 0.0376        | 31.95  | 160  | 0.7286          | 0.8473 | 0.8475   |
| 0.0185        | 35.95  | 180  | 0.6779          | 0.8719 | 0.8723   |
| 0.0752        | 39.95  | 200  | 0.7096          | 0.8578 | 0.8511   |
| 0.0266        | 43.95  | 220  | 0.7655          | 0.8596 | 0.8546   |
| 0.0078        | 47.95  | 240  | 0.7623          | 0.8563 | 0.8511   |
| 0.007         | 51.95  | 260  | 0.6620          | 0.8794 | 0.8759   |
| 0.0047        | 55.95  | 280  | 0.5936          | 0.9045 | 0.9007   |
| 0.0067        | 59.95  | 300  | 0.8279          | 0.8546 | 0.8617   |
| 0.0394        | 63.95  | 320  | 0.8766          | 0.8359 | 0.8227   |
| 0.0051        | 67.95  | 340  | 0.8097          | 0.8483 | 0.8475   |
| 0.0095        | 71.95  | 360  | 0.6095          | 0.9083 | 0.9078   |
| 0.0026        | 75.95  | 380  | 0.5286          | 0.8889 | 0.8865   |
| 0.0023        | 79.95  | 400  | 0.7218          | 0.8926 | 0.8936   |
| 0.0023        | 83.95  | 420  | 0.6551          | 0.8997 | 0.8972   |
| 0.0027        | 87.95  | 440  | 0.6664          | 0.8848 | 0.8794   |
| 0.0019        | 91.95  | 460  | 0.5344          | 0.9032 | 0.9043   |
| 0.002         | 95.95  | 480  | 0.5863          | 0.8983 | 0.9007   |
| 0.0015        | 99.95  | 500  | 0.5715          | 0.9047 | 0.9043   |
| 0.0016        | 103.95 | 520  | 0.5615          | 0.8956 | 0.8936   |
| 0.0014        | 107.95 | 540  | 0.6353          | 0.8965 | 0.8936   |
| 0.0014        | 111.95 | 560  | 0.5593          | 0.9041 | 0.9007   |
| 0.0013        | 115.95 | 580  | 0.6041          | 0.8977 | 0.8936   |
| 0.0013        | 119.95 | 600  | 0.5794          | 0.9026 | 0.9007   |
| 0.0012        | 123.95 | 620  | 0.6858          | 0.9003 | 0.8972   |
| 0.0013        | 127.95 | 640  | 0.6730          | 0.9002 | 0.8972   |
| 0.0013        | 131.95 | 660  | 0.5707          | 0.9146 | 0.9113   |
| 0.0012        | 135.95 | 680  | 0.5604          | 0.9153 | 0.9113   |
| 0.0019        | 139.95 | 700  | 0.5468          | 0.9114 | 0.9078   |
| 0.0015        | 143.95 | 720  | 0.5361          | 0.9144 | 0.9113   |
| 0.0012        | 147.95 | 740  | 0.5337          | 0.9144 | 0.9113   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"language": ["en-US"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_w2v2_minds14.en-US", "results": []}]}
transformers pytorch tensorboard wav2vec2 minds14 google/xtreme_s generated_from_trainer dataset:xtreme_s license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 

xtreme\_s\_w2v2\_minds14.en-US
==============================


This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the GOOGLE/XTREME\_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5337
* F1: 0.9144
* Accuracy: 0.9113


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: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 150.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 150.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 150.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #minds14 #google/xtreme_s #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 150.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1