
<!-- 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-base-uncased-finetuned-mrpc

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5682
- Accuracy: 0.7164
- F1: 0.2022

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5789        | 1.0   | 635   | 0.5764          | 0.7055   | 0.0    |
| 0.543         | 2.0   | 1270  | 0.5682          | 0.7164   | 0.2022 |
| 0.4661        | 3.0   | 1905  | 0.6358          | 0.7164   | 0.2812 |
| 0.2338        | 4.0   | 2540  | 0.9347          | 0.6844   | 0.4670 |
| 0.1538        | 5.0   | 3175  | 1.3556          | 0.6758   | 0.4740 |
| 0.1067        | 6.0   | 3810  | 1.6163          | 0.7016   | 0.3322 |
| 0.0928        | 7.0   | 4445  | 2.0786          | 0.6984   | 0.3609 |
| 0.0438        | 8.0   | 5080  | 2.1976          | 0.6945   | 0.4309 |
| 0.0312        | 9.0   | 5715  | 2.1931          | 0.6969   | 0.4209 |
| 0.0311        | 10.0  | 6350  | 2.4030          | 0.6883   | 0.4158 |
| 0.0281        | 11.0  | 6985  | 2.3715          | 0.7148   | 0.3739 |
| 0.0166        | 12.0  | 7620  | 2.6843          | 0.6984   | 0.3390 |
| 0.0167        | 13.0  | 8255  | 2.7291          | 0.6922   | 0.3604 |
| 0.0181        | 14.0  | 8890  | 2.7929          | 0.6906   | 0.3851 |
| 0.0147        | 15.0  | 9525  | 2.8976          | 0.7117   | 0.3303 |
| 0.0103        | 16.0  | 10160 | 3.0229          | 0.6859   | 0.3964 |
| 0.0047        | 17.0  | 10795 | 3.0616          | 0.6836   | 0.3817 |
| 0.0136        | 18.0  | 11430 | 3.0513          | 0.6875   | 0.3730 |
| 0.005         | 19.0  | 12065 | 3.0634          | 0.6930   | 0.3732 |
| 0.0042        | 20.0  | 12700 | 3.0611          | 0.7      | 0.3642 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 1.18.4
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-mrpc
======================================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5682
* Accuracy: 0.7164
* F1: 0.2022


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu117
* Datasets 1.18.4
* Tokenizers 0.12.1