
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert_add_GLUE_Experiment_mnli_96

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: 1.0256
- Accuracy: 0.5004

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0987        | 1.0   | 1534  | 1.0980          | 0.3545   |
| 1.0979        | 2.0   | 3068  | 1.0942          | 0.3580   |
| 1.0897        | 3.0   | 4602  | 1.0896          | 0.3706   |
| 1.0817        | 4.0   | 6136  | 1.0769          | 0.3991   |
| 1.072         | 5.0   | 7670  | 1.0680          | 0.4146   |
| 1.0603        | 6.0   | 9204  | 1.0700          | 0.4174   |
| 1.0515        | 7.0   | 10738 | 1.0655          | 0.4179   |
| 1.0441        | 8.0   | 12272 | 1.0546          | 0.4335   |
| 1.038         | 9.0   | 13806 | 1.0751          | 0.4059   |
| 1.0344        | 10.0  | 15340 | 1.0554          | 0.4363   |
| 1.0275        | 11.0  | 16874 | 1.0736          | 0.4207   |
| 1.0225        | 12.0  | 18408 | 1.0662          | 0.4295   |
| 1.0169        | 13.0  | 19942 | 1.0544          | 0.4421   |
| 1.0111        | 14.0  | 21476 | 1.0635          | 0.4411   |
| 1.0043        | 15.0  | 23010 | 1.0505          | 0.4567   |
| 0.9986        | 16.0  | 24544 | 1.0402          | 0.4643   |
| 0.9925        | 17.0  | 26078 | 1.0531          | 0.4545   |
| 0.9861        | 18.0  | 27612 | 1.0431          | 0.4675   |
| 0.9781        | 19.0  | 29146 | 1.0361          | 0.4801   |
| 0.9673        | 20.0  | 30680 | 1.0301          | 0.4879   |
| 0.9552        | 21.0  | 32214 | 1.0327          | 0.4908   |
| 0.9467        | 22.0  | 33748 | 1.0248          | 0.5013   |
| 0.9396        | 23.0  | 35282 | 1.0297          | 0.4977   |
| 0.9328        | 24.0  | 36816 | 1.0237          | 0.5025   |
| 0.9277        | 25.0  | 38350 | 1.0384          | 0.5010   |
| 0.9228        | 26.0  | 39884 | 1.0374          | 0.5037   |
| 0.918         | 27.0  | 41418 | 1.0242          | 0.5006   |
| 0.9128        | 28.0  | 42952 | 1.0248          | 0.5060   |
| 0.9087        | 29.0  | 44486 | 1.0283          | 0.5027   |


### 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_96", "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": 0.500406834825061, "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\_96
===========================================


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: 1.0256
* Accuracy: 0.5004


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