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# mobilebert_sa_GLUE_Experiment_logit_kd_mnli_256

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2282
- Accuracy: 0.6120

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6433        | 1.0   | 3068  | 1.4078          | 0.5457   |
| 1.4683        | 2.0   | 6136  | 1.3590          | 0.5658   |
| 1.4077        | 3.0   | 9204  | 1.3106          | 0.5772   |
| 1.3591        | 4.0   | 12272 | 1.2971          | 0.5904   |
| 1.3213        | 5.0   | 15340 | 1.2764          | 0.5957   |
| 1.2849        | 6.0   | 18408 | 1.2562          | 0.6029   |
| 1.2475        | 7.0   | 21476 | 1.2524          | 0.6038   |
| 1.2073        | 8.0   | 24544 | 1.2384          | 0.6066   |
| 1.1713        | 9.0   | 27612 | 1.2377          | 0.6109   |
| 1.1371        | 10.0  | 30680 | 1.2228          | 0.6077   |
| 1.1069        | 11.0  | 33748 | 1.2126          | 0.6196   |
| 1.0775        | 12.0  | 36816 | 1.2232          | 0.6271   |
| 1.0491        | 13.0  | 39884 | 1.2440          | 0.6110   |
| 1.0228        | 14.0  | 42952 | 1.2741          | 0.6079   |
| 0.9977        | 15.0  | 46020 | 1.2448          | 0.6158   |
| 0.974         | 16.0  | 49088 | 1.3261          | 0.6206   |


### 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"], "model-index": [{"name": "mobilebert_sa_GLUE_Experiment_logit_kd_mnli_256", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "config": "mnli", "split": "validation_matched", "args": "mnli"}, "metrics": [{"type": "accuracy", "value": 0.6119812855980472, "name": "Accuracy"}]}]}]}
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\_mnli\_256
======================================================


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


* Loss: 1.2282
* Accuracy: 0.6120


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