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# fine_tuned_bert_dreadit

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6081
- Accuracy: 0.7528

## 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: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0037        | 1.0   | 178   | 1.8515          | 0.7163   |
| 0.0017        | 2.0   | 356   | 1.7404          | 0.7163   |
| 0.001         | 3.0   | 534   | 1.2895          | 0.7921   |
| 0.0012        | 4.0   | 712   | 1.3320          | 0.7669   |
| 0.0005        | 5.0   | 890   | 1.3646          | 0.7949   |
| 0.0002        | 6.0   | 1068  | 1.5997          | 0.7809   |
| 0.0001        | 7.0   | 1246  | 1.5772          | 0.7753   |
| 0.0003        | 8.0   | 1424  | 1.7599          | 0.7556   |
| 0.0001        | 9.0   | 1602  | 1.7494          | 0.7640   |
| 0.0001        | 10.0  | 1780  | 1.9942          | 0.7556   |
| 0.0001        | 11.0  | 1958  | 1.9370          | 0.75     |
| 0.0           | 12.0  | 2136  | 1.9671          | 0.7781   |
| 0.0001        | 13.0  | 2314  | 2.1223          | 0.7640   |
| 0.0           | 14.0  | 2492  | 2.1653          | 0.7472   |
| 0.0001        | 15.0  | 2670  | 1.9924          | 0.75     |
| 0.0           | 16.0  | 2848  | 2.1778          | 0.7528   |
| 0.0           | 17.0  | 3026  | 2.3010          | 0.7612   |
| 0.0           | 18.0  | 3204  | 2.2210          | 0.7669   |
| 0.0           | 19.0  | 3382  | 2.3333          | 0.7556   |
| 0.0           | 20.0  | 3560  | 1.8684          | 0.7697   |
| 0.0976        | 21.0  | 3738  | 1.9417          | 0.7584   |
| 0.0           | 22.0  | 3916  | 2.1385          | 0.7472   |
| 0.0           | 23.0  | 4094  | 1.9774          | 0.7669   |
| 0.0           | 24.0  | 4272  | 2.0778          | 0.75     |
| 0.0001        | 25.0  | 4450  | 2.4343          | 0.7331   |
| 0.0           | 26.0  | 4628  | 2.1331          | 0.7528   |
| 0.0           | 27.0  | 4806  | 2.2511          | 0.7640   |
| 0.0           | 28.0  | 4984  | 2.2422          | 0.7584   |
| 0.0           | 29.0  | 5162  | 2.1228          | 0.7669   |
| 0.0006        | 30.0  | 5340  | 2.0973          | 0.7725   |
| 0.0           | 31.0  | 5518  | 1.9392          | 0.7809   |
| 0.0           | 32.0  | 5696  | 2.2996          | 0.7107   |
| 0.4186        | 33.0  | 5874  | 2.2191          | 0.7584   |
| 0.0           | 34.0  | 6052  | 2.2233          | 0.75     |
| 0.0           | 35.0  | 6230  | 2.2263          | 0.7584   |
| 0.0           | 36.0  | 6408  | 2.2205          | 0.7584   |
| 0.0           | 37.0  | 6586  | 2.4488          | 0.7444   |
| 0.0           | 38.0  | 6764  | 2.5616          | 0.7360   |
| 0.0           | 39.0  | 6942  | 2.5941          | 0.7416   |
| 0.0           | 40.0  | 7120  | 2.5129          | 0.7528   |
| 0.0           | 41.0  | 7298  | 2.4978          | 0.7360   |
| 0.0           | 42.0  | 7476  | 2.3089          | 0.7528   |
| 0.0           | 43.0  | 7654  | 2.5056          | 0.7472   |
| 0.0           | 44.0  | 7832  | 2.5786          | 0.7416   |
| 0.0           | 45.0  | 8010  | 2.2956          | 0.7640   |
| 0.0           | 46.0  | 8188  | 2.5265          | 0.7472   |
| 0.0           | 47.0  | 8366  | 2.4396          | 0.7584   |
| 0.0           | 48.0  | 8544  | 2.5547          | 0.7472   |
| 0.0           | 49.0  | 8722  | 2.5556          | 0.7528   |
| 0.0           | 50.0  | 8900  | 2.5732          | 0.7528   |
| 0.0           | 51.0  | 9078  | 2.5062          | 0.7556   |
| 0.0           | 52.0  | 9256  | 2.5504          | 0.7528   |
| 0.0           | 53.0  | 9434  | 2.5602          | 0.7528   |
| 0.0           | 54.0  | 9612  | 2.5627          | 0.7472   |
| 0.0           | 55.0  | 9790  | 2.6575          | 0.75     |
| 0.0           | 56.0  | 9968  | 2.6239          | 0.7528   |
| 0.0           | 57.0  | 10146 | 2.4757          | 0.7697   |
| 0.0           | 58.0  | 10324 | 2.4862          | 0.7612   |
| 0.0           | 59.0  | 10502 | 3.2968          | 0.6938   |
| 0.0           | 60.0  | 10680 | 2.5265          | 0.7472   |
| 0.0           | 61.0  | 10858 | 2.1426          | 0.7978   |
| 0.0           | 62.0  | 11036 | 2.4674          | 0.7640   |
| 0.0           | 63.0  | 11214 | 2.3496          | 0.7640   |
| 0.0           | 64.0  | 11392 | 2.4010          | 0.7556   |
| 0.0           | 65.0  | 11570 | 2.4081          | 0.7725   |
| 0.0           | 66.0  | 11748 | 2.4022          | 0.7753   |
| 0.0           | 67.0  | 11926 | 2.2982          | 0.7753   |
| 0.0           | 68.0  | 12104 | 2.4628          | 0.7612   |
| 0.0           | 69.0  | 12282 | 2.5764          | 0.7640   |
| 0.0           | 70.0  | 12460 | 2.4056          | 0.7781   |
| 0.0           | 71.0  | 12638 | 2.3265          | 0.7865   |
| 0.0           | 72.0  | 12816 | 2.5182          | 0.7640   |
| 0.0           | 73.0  | 12994 | 2.3872          | 0.7556   |
| 0.0           | 74.0  | 13172 | 2.7281          | 0.7388   |
| 0.0           | 75.0  | 13350 | 2.4907          | 0.7612   |
| 0.0           | 76.0  | 13528 | 2.5323          | 0.7584   |
| 0.0           | 77.0  | 13706 | 2.2055          | 0.7837   |
| 0.0           | 78.0  | 13884 | 2.2227          | 0.7865   |
| 0.0           | 79.0  | 14062 | 2.2794          | 0.7753   |
| 0.0           | 80.0  | 14240 | 2.2886          | 0.7753   |
| 0.0           | 81.0  | 14418 | 2.8320          | 0.7444   |
| 0.0           | 82.0  | 14596 | 2.8252          | 0.7472   |
| 0.0           | 83.0  | 14774 | 2.2986          | 0.7837   |
| 0.0           | 84.0  | 14952 | 2.7879          | 0.7416   |
| 0.0           | 85.0  | 15130 | 2.7926          | 0.7416   |
| 0.0           | 86.0  | 15308 | 2.7656          | 0.7472   |
| 0.0           | 87.0  | 15486 | 2.7336          | 0.7444   |
| 0.0           | 88.0  | 15664 | 2.7320          | 0.7444   |
| 0.0           | 89.0  | 15842 | 2.7402          | 0.7444   |
| 0.0           | 90.0  | 16020 | 2.7415          | 0.7444   |
| 0.0           | 91.0  | 16198 | 2.7406          | 0.7444   |
| 0.0           | 92.0  | 16376 | 2.7327          | 0.7444   |
| 0.0           | 93.0  | 16554 | 2.4082          | 0.7781   |
| 0.0           | 94.0  | 16732 | 2.4077          | 0.7753   |
| 0.0           | 95.0  | 16910 | 2.4185          | 0.7781   |
| 0.0           | 96.0  | 17088 | 2.6096          | 0.7528   |
| 0.0           | 97.0  | 17266 | 2.5907          | 0.7669   |
| 0.0           | 98.0  | 17444 | 2.6030          | 0.7556   |
| 0.0           | 99.0  | 17622 | 2.6081          | 0.7528   |
| 0.0           | 100.0 | 17800 | 2.6081          | 0.7528   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "fine_tuned_bert_dreadit", "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 

fine\_tuned\_bert\_dreadit
==========================


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


* Loss: 2.6081
* Accuracy: 0.7528


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: 100


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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: 100 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 100 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 100### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2