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# distilbert_add_GLUE_Experiment_mnli_192

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9590
- Accuracy: 0.5259

## 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: 256
- eval_batch_size: 256
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.096         | 1.0   | 1534  | 1.0536          | 0.4466   |
| 1.0362        | 2.0   | 3068  | 1.0527          | 0.4579   |
| 1.0213        | 3.0   | 4602  | 1.0341          | 0.4606   |
| 1.0085        | 4.0   | 6136  | 1.0170          | 0.4810   |
| 0.9971        | 5.0   | 7670  | 1.0013          | 0.4868   |
| 0.9877        | 6.0   | 9204  | 0.9913          | 0.4950   |
| 0.9805        | 7.0   | 10738 | 0.9872          | 0.4986   |
| 0.9726        | 8.0   | 12272 | 0.9822          | 0.5019   |
| 0.9658        | 9.0   | 13806 | 0.9812          | 0.5115   |
| 0.9566        | 10.0  | 15340 | 0.9761          | 0.5179   |
| 0.9439        | 11.0  | 16874 | 0.9650          | 0.5261   |
| 0.9336        | 12.0  | 18408 | 0.9616          | 0.5283   |
| 0.9232        | 13.0  | 19942 | 0.9620          | 0.5306   |
| 0.9145        | 14.0  | 21476 | 0.9663          | 0.5329   |
| 0.9056        | 15.0  | 23010 | 0.9491          | 0.5408   |
| 0.8986        | 16.0  | 24544 | 0.9567          | 0.5389   |
| 0.8903        | 17.0  | 26078 | 0.9488          | 0.5382   |
| 0.8835        | 18.0  | 27612 | 0.9739          | 0.5202   |
| 0.8769        | 19.0  | 29146 | 0.9761          | 0.5382   |
| 0.8701        | 20.0  | 30680 | 0.9504          | 0.5439   |
| 0.8633        | 21.0  | 32214 | 1.0074          | 0.5265   |
| 0.8576        | 22.0  | 33748 | 0.9874          | 0.5392   |


### 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"], "model-index": [{"name": "distilbert_add_GLUE_Experiment_mnli_192", "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": 0.5259357200976403, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert\_add\_GLUE\_Experiment\_mnli\_192
============================================


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


* Loss: 0.9590
* Accuracy: 0.5259


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: 256
* eval\_batch\_size: 256
* 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: 256
* eval\_batch\_size: 256
* 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 #distilbert #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: 256
* eval\_batch\_size: 256
* 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 #distilbert #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: 256
* eval\_batch\_size: 256
* 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