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# distilbert_add_GLUE_Experiment_mnli_256

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: 0.9622
- Accuracy: 0.5456

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0905        | 1.0   | 1534  | 1.0415          | 0.4633   |
| 1.0344        | 2.0   | 3068  | 1.0571          | 0.4569   |
| 1.0137        | 3.0   | 4602  | 1.0080          | 0.4888   |
| 0.9902        | 4.0   | 6136  | 0.9876          | 0.5090   |
| 0.9733        | 5.0   | 7670  | 0.9744          | 0.5134   |
| 0.9596        | 6.0   | 9204  | 0.9726          | 0.5138   |
| 0.9484        | 7.0   | 10738 | 0.9684          | 0.5233   |
| 0.938         | 8.0   | 12272 | 0.9715          | 0.5150   |
| 0.9279        | 9.0   | 13806 | 0.9707          | 0.5237   |
| 0.9188        | 10.0  | 15340 | 0.9580          | 0.5319   |
| 0.9102        | 11.0  | 16874 | 0.9561          | 0.5396   |
| 0.9011        | 12.0  | 18408 | 0.9594          | 0.5368   |
| 0.893         | 13.0  | 19942 | 0.9641          | 0.5345   |
| 0.8845        | 14.0  | 21476 | 0.9671          | 0.5367   |
| 0.8751        | 15.0  | 23010 | 0.9553          | 0.5388   |
| 0.8662        | 16.0  | 24544 | 0.9601          | 0.5447   |
| 0.8573        | 17.0  | 26078 | 0.9519          | 0.5473   |
| 0.849         | 18.0  | 27612 | 0.9814          | 0.5410   |
| 0.8404        | 19.0  | 29146 | 0.9733          | 0.5464   |
| 0.832         | 20.0  | 30680 | 0.9671          | 0.5492   |
| 0.8233        | 21.0  | 32214 | 0.9864          | 0.5509   |
| 0.8144        | 22.0  | 33748 | 0.9925          | 0.5536   |


### 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_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": 0.5455655004068348, "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\_256
============================================


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: 0.9622
* Accuracy: 0.5456


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