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# mobilebert_sa_GLUE_Experiment_logit_kd_mrpc

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5133
- Accuracy: 0.6740
- F1: 0.7772
- Combined Score: 0.7256

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6228        | 1.0   | 29   | 0.5556          | 0.6838   | 0.8122 | 0.7480         |
| 0.611         | 2.0   | 58   | 0.5551          | 0.6838   | 0.8122 | 0.7480         |
| 0.6095        | 3.0   | 87   | 0.5538          | 0.6838   | 0.8122 | 0.7480         |
| 0.6062        | 4.0   | 116  | 0.5503          | 0.6838   | 0.8122 | 0.7480         |
| 0.5825        | 5.0   | 145  | 0.5262          | 0.6985   | 0.8167 | 0.7576         |
| 0.4981        | 6.0   | 174  | 0.5197          | 0.6936   | 0.8038 | 0.7487         |
| 0.468         | 7.0   | 203  | 0.5133          | 0.6740   | 0.7772 | 0.7256         |
| 0.3901        | 8.0   | 232  | 0.5382          | 0.6838   | 0.7757 | 0.7297         |
| 0.323         | 9.0   | 261  | 0.6140          | 0.6789   | 0.7657 | 0.7223         |
| 0.2674        | 10.0  | 290  | 0.5512          | 0.6740   | 0.7687 | 0.7214         |
| 0.2396        | 11.0  | 319  | 0.6467          | 0.6667   | 0.7631 | 0.7149         |
| 0.2127        | 12.0  | 348  | 0.7811          | 0.6716   | 0.7690 | 0.7203         |


### 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_mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "config": "mrpc", "split": "validation", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.6740196078431373, "name": "Accuracy"}, {"type": "f1", "value": 0.7772194304857621, "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\_mrpc
=================================================


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


* Loss: 0.5133
* Accuracy: 0.6740
* F1: 0.7772
* Combined Score: 0.7256


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