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# distilbert-base-uncased-finetuned-as_sentences

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: 0.0627
- Accuracy: 0.9733
- F1: 0.9733

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6987        | 1.0   | 11   | 0.6958          | 0.46     | 0.3025 |
| 0.6851        | 2.0   | 22   | 0.6715          | 0.5667   | 0.4954 |
| 0.6315        | 3.0   | 33   | 0.4515          | 0.88     | 0.8791 |
| 0.4086        | 4.0   | 44   | 0.1662          | 0.96     | 0.9599 |
| 0.136         | 5.0   | 55   | 0.0857          | 0.9667   | 0.9666 |
| 0.0955        | 6.0   | 66   | 0.0661          | 0.9733   | 0.9733 |
| 0.022         | 7.0   | 77   | 0.0569          | 0.9667   | 0.9666 |
| 0.0272        | 8.0   | 88   | 0.0626          | 0.9667   | 0.9666 |
| 0.0346        | 9.0   | 99   | 0.0818          | 0.9667   | 0.9666 |
| 0.0157        | 10.0  | 110  | 0.0649          | 0.9667   | 0.9666 |
| 0.0232        | 11.0  | 121  | 0.1416          | 0.9533   | 0.9531 |
| 0.0202        | 12.0  | 132  | 0.0652          | 0.9733   | 0.9733 |
| 0.0069        | 13.0  | 143  | 0.0764          | 0.96     | 0.9599 |
| 0.0032        | 14.0  | 154  | 0.0842          | 0.9667   | 0.9666 |
| 0.0052        | 15.0  | 165  | 0.0697          | 0.9667   | 0.9666 |
| 0.0028        | 16.0  | 176  | 0.0773          | 0.9667   | 0.9666 |
| 0.0066        | 17.0  | 187  | 0.0809          | 0.9667   | 0.9667 |
| 0.0022        | 18.0  | 198  | 0.0569          | 0.9667   | 0.9666 |
| 0.002         | 19.0  | 209  | 0.0537          | 0.9733   | 0.9733 |
| 0.0016        | 20.0  | 220  | 0.0502          | 0.9733   | 0.9733 |
| 0.0015        | 21.0  | 231  | 0.0460          | 0.9733   | 0.9733 |
| 0.0013        | 22.0  | 242  | 0.0451          | 0.9733   | 0.9733 |
| 0.0013        | 23.0  | 253  | 0.0448          | 0.9733   | 0.9733 |
| 0.0012        | 24.0  | 264  | 0.0450          | 0.9733   | 0.9733 |
| 0.0012        | 25.0  | 275  | 0.0457          | 0.9733   | 0.9733 |
| 0.0011        | 26.0  | 286  | 0.0465          | 0.9733   | 0.9733 |
| 0.0011        | 27.0  | 297  | 0.0466          | 0.9733   | 0.9733 |
| 0.001         | 28.0  | 308  | 0.0613          | 0.9667   | 0.9666 |
| 0.001         | 29.0  | 319  | 0.0658          | 0.9667   | 0.9666 |
| 0.0009        | 30.0  | 330  | 0.0674          | 0.9667   | 0.9666 |
| 0.0008        | 31.0  | 341  | 0.0693          | 0.9667   | 0.9666 |
| 0.0009        | 32.0  | 352  | 0.0711          | 0.9667   | 0.9666 |
| 0.0008        | 33.0  | 363  | 0.0718          | 0.9667   | 0.9666 |
| 0.0028        | 34.0  | 374  | 0.0824          | 0.9667   | 0.9667 |
| 0.0011        | 35.0  | 385  | 0.0884          | 0.9667   | 0.9666 |
| 0.0008        | 36.0  | 396  | 0.1060          | 0.9667   | 0.9666 |
| 0.0009        | 37.0  | 407  | 0.0875          | 0.96     | 0.9599 |
| 0.0015        | 38.0  | 418  | 0.0623          | 0.9667   | 0.9666 |
| 0.0007        | 39.0  | 429  | 0.0610          | 0.9733   | 0.9733 |
| 0.0007        | 40.0  | 440  | 0.0614          | 0.9733   | 0.9733 |
| 0.0007        | 41.0  | 451  | 0.0617          | 0.9733   | 0.9733 |
| 0.0007        | 42.0  | 462  | 0.0618          | 0.9733   | 0.9733 |
| 0.0006        | 43.0  | 473  | 0.0620          | 0.9733   | 0.9733 |
| 0.0006        | 44.0  | 484  | 0.0621          | 0.9733   | 0.9733 |
| 0.0006        | 45.0  | 495  | 0.0622          | 0.9733   | 0.9733 |
| 0.0006        | 46.0  | 506  | 0.0624          | 0.9733   | 0.9733 |
| 0.0006        | 47.0  | 517  | 0.0625          | 0.9733   | 0.9733 |
| 0.0006        | 48.0  | 528  | 0.0626          | 0.9733   | 0.9733 |
| 0.0006        | 49.0  | 539  | 0.0627          | 0.9733   | 0.9733 |
| 0.0006        | 50.0  | 550  | 0.0627          | 0.9733   | 0.9733 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3

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

distilbert-base-uncased-finetuned-as\_sentences
===============================================


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: 0.0627
* Accuracy: 0.9733
* F1: 0.9733


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cu118
* Datasets 2.13.1
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cu118
* Datasets 2.13.1
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.30.2
* Pytorch 2.0.1+cu118
* Datasets 2.13.1
* Tokenizers 0.13.3