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# fine_tune_results

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: 0.0821

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 1    | 0.5517          |
| No log        | 2.0   | 2    | 0.4685          |
| No log        | 3.0   | 3    | 0.4025          |
| No log        | 4.0   | 4    | 0.3522          |
| No log        | 5.0   | 5    | 0.3154          |
| No log        | 6.0   | 6    | 0.2895          |
| No log        | 7.0   | 7    | 0.2715          |
| No log        | 8.0   | 8    | 0.2579          |
| No log        | 9.0   | 9    | 0.2464          |
| No log        | 10.0  | 10   | 0.2362          |
| No log        | 11.0  | 11   | 0.2270          |
| No log        | 12.0  | 12   | 0.2188          |
| No log        | 13.0  | 13   | 0.2114          |
| No log        | 14.0  | 14   | 0.2049          |
| No log        | 15.0  | 15   | 0.1988          |
| No log        | 16.0  | 16   | 0.1926          |
| No log        | 17.0  | 17   | 0.1862          |
| No log        | 18.0  | 18   | 0.1793          |
| No log        | 19.0  | 19   | 0.1720          |
| No log        | 20.0  | 20   | 0.1644          |
| No log        | 21.0  | 21   | 0.1565          |
| No log        | 22.0  | 22   | 0.1485          |
| No log        | 23.0  | 23   | 0.1406          |
| No log        | 24.0  | 24   | 0.1330          |
| No log        | 25.0  | 25   | 0.1259          |
| No log        | 26.0  | 26   | 0.1193          |
| No log        | 27.0  | 27   | 0.1133          |
| No log        | 28.0  | 28   | 0.1080          |
| No log        | 29.0  | 29   | 0.1032          |
| No log        | 30.0  | 30   | 0.0991          |
| No log        | 31.0  | 31   | 0.0955          |
| No log        | 32.0  | 32   | 0.0925          |
| No log        | 33.0  | 33   | 0.0900          |
| No log        | 34.0  | 34   | 0.0878          |
| No log        | 35.0  | 35   | 0.0860          |
| No log        | 36.0  | 36   | 0.0847          |
| No log        | 37.0  | 37   | 0.0836          |
| No log        | 38.0  | 38   | 0.0828          |
| No log        | 39.0  | 39   | 0.0823          |
| No log        | 40.0  | 40   | 0.0821          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "fine_tune_results", "results": []}]}
transformers pytorch distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

fine\_tune\_results
===================


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: 0.0821


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: 40


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cpu
* 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: 40 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #token-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: 40 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #token-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: 40### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cpu
* Datasets 2.10.1
* Tokenizers 0.13.2