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# my_awesome_model

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: 1.0970
- Accuracy: 0.8681
- F1: 0.8376

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 167  | 0.3828          | 0.8501   | 0.8031 |
| No log        | 2.0   | 334  | 0.4787          | 0.8456   | 0.8275 |
| 0.2101        | 3.0   | 501  | 0.6186          | 0.8666   | 0.8367 |
| 0.2101        | 4.0   | 668  | 0.7201          | 0.8546   | 0.8265 |
| 0.2101        | 5.0   | 835  | 0.7675          | 0.8651   | 0.8346 |
| 0.0339        | 6.0   | 1002 | 0.8561          | 0.8681   | 0.8434 |
| 0.0339        | 7.0   | 1169 | 0.8898          | 0.8681   | 0.8382 |
| 0.0339        | 8.0   | 1336 | 0.9854          | 0.8711   | 0.8436 |
| 0.0069        | 9.0   | 1503 | 0.9919          | 0.8711   | 0.8407 |
| 0.0069        | 10.0  | 1670 | 1.0695          | 0.8561   | 0.8280 |
| 0.0069        | 11.0  | 1837 | 1.0542          | 0.8666   | 0.8349 |
| 0.0007        | 12.0  | 2004 | 1.0896          | 0.8681   | 0.8370 |
| 0.0007        | 13.0  | 2171 | 1.1001          | 0.8666   | 0.8349 |
| 0.0007        | 14.0  | 2338 | 1.0888          | 0.8606   | 0.8312 |
| 0.0012        | 15.0  | 2505 | 1.0970          | 0.8681   | 0.8376 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "my_awesome_model", "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 

my\_awesome\_model
==================


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: 1.0970
* Accuracy: 0.8681
* F1: 0.8376


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: 15


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3