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# mobilebert_sa_GLUE_Experiment_logit_kd_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: 0.6021
- Accuracy: 0.8081
- F1: 0.7357
- Combined Score: 0.7719

## 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.9285        | 1.0   | 2843  | 0.8489          | 0.7517   | 0.6660 | 0.7089         |
| 0.7919        | 2.0   | 5686  | 0.7104          | 0.7735   | 0.6483 | 0.7109         |
| 0.6916        | 3.0   | 8529  | 0.6615          | 0.7900   | 0.6994 | 0.7447         |
| 0.6382        | 4.0   | 11372 | 0.6606          | 0.7899   | 0.6842 | 0.7370         |
| 0.6003        | 5.0   | 14215 | 0.6277          | 0.7988   | 0.7181 | 0.7585         |
| 0.5696        | 6.0   | 17058 | 0.6174          | 0.7980   | 0.7058 | 0.7519         |
| 0.5434        | 7.0   | 19901 | 0.6062          | 0.8036   | 0.7266 | 0.7651         |
| 0.5186        | 8.0   | 22744 | 0.6182          | 0.7995   | 0.7232 | 0.7614         |
| 0.4968        | 9.0   | 25587 | 0.6087          | 0.8052   | 0.7257 | 0.7654         |
| 0.4758        | 10.0  | 28430 | 0.6035          | 0.8073   | 0.7359 | 0.7716         |
| 0.456         | 11.0  | 31273 | 0.6021          | 0.8081   | 0.7357 | 0.7719         |
| 0.4361        | 12.0  | 34116 | 0.6137          | 0.8070   | 0.7366 | 0.7718         |
| 0.4186        | 13.0  | 36959 | 0.6282          | 0.8076   | 0.7416 | 0.7746         |
| 0.4009        | 14.0  | 39802 | 0.6183          | 0.8093   | 0.7445 | 0.7769         |
| 0.3846        | 15.0  | 42645 | 0.6196          | 0.8057   | 0.7483 | 0.7770         |
| 0.3699        | 16.0  | 45488 | 0.6381          | 0.8122   | 0.7426 | 0.7774         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- 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_logit_kd_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": 0.8081127875340094, "name": "Accuracy"}, {"type": "f1", "value": 0.7357269382749694, "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\_logit\_kd\_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: 0.6021
* Accuracy: 0.8081
* F1: 0.7357
* Combined Score: 0.7719


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


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* 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 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* 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 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* 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### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2