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# mobilebert_sa_GLUE_Experiment_qqp

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4287
- Accuracy: 0.8007
- F1: 0.7301
- Combined Score: 0.7654

## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5237        | 1.0   | 2843  | 0.5087          | 0.7472   | 0.6697 | 0.7085         |
| 0.4614        | 2.0   | 5686  | 0.4697          | 0.7754   | 0.6746 | 0.7250         |
| 0.4287        | 3.0   | 8529  | 0.4508          | 0.7853   | 0.6893 | 0.7373         |
| 0.4089        | 4.0   | 11372 | 0.4493          | 0.7925   | 0.7151 | 0.7538         |
| 0.3904        | 5.0   | 14215 | 0.4361          | 0.7984   | 0.7222 | 0.7603         |
| 0.3752        | 6.0   | 17058 | 0.4332          | 0.8023   | 0.7215 | 0.7619         |
| 0.3592        | 7.0   | 19901 | 0.4287          | 0.8007   | 0.7301 | 0.7654         |
| 0.3458        | 8.0   | 22744 | 0.4337          | 0.8005   | 0.7324 | 0.7664         |
| 0.3326        | 9.0   | 25587 | 0.4340          | 0.8006   | 0.7362 | 0.7684         |
| 0.3201        | 10.0  | 28430 | 0.4464          | 0.8028   | 0.7417 | 0.7722         |
| 0.3092        | 11.0  | 31273 | 0.4615          | 0.8037   | 0.7196 | 0.7617         |
| 0.2984        | 12.0  | 34116 | 0.4763          | 0.8047   | 0.7326 | 0.7687         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "mobilebert_sa_GLUE_Experiment_qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "config": "qqp", "split": "validation", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.8006678209250556, "name": "Accuracy"}, {"type": "f1", "value": 0.7300800482298958, "name": "F1"}]}]}]}
transformers pytorch tensorboard mobilebert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

mobilebert\_sa\_GLUE\_Experiment\_qqp
=====================================


This model is a fine-tuned version of google/mobilebert-uncased on the GLUE QQP dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4287
* Accuracy: 0.8007
* F1: 0.7301
* Combined Score: 0.7654


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: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.8.0
* Tokenizers 0.13.2