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# distilbert-base-uncased-finetuned-Multi_classification

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: 0.5588
- Accuracy: 0.7266
- Macro Averaged Precision: 0.6830
- Micro Averaged Precision: 0.7266
- Macro Averaged Recall: 0.5652
- Micro Averaged Recall: 0.7266
- Macro Averaged F1: 0.5513
- Micro Averaged F1: 0.7266

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Averaged Precision | Micro Averaged Precision | Macro Averaged Recall | Micro Averaged Recall | Macro Averaged F1 | Micro Averaged F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------:|:------------------------:|:---------------------:|:---------------------:|:-----------------:|:-----------------:|
| 0.5811        | 1.0   | 635  | 0.5745          | 0.7055   | 0.3527                   | 0.7055                   | 0.5                   | 0.7055                | 0.4137            | 0.7055            |
| 0.5467        | 2.0   | 1270 | 0.5588          | 0.7266   | 0.6830                   | 0.7266                   | 0.5652                | 0.7266                | 0.5513            | 0.7266            |
| 0.4724        | 3.0   | 1905 | 0.6347          | 0.7109   | 0.6328                   | 0.7109                   | 0.5873                | 0.7109                | 0.5906            | 0.7109            |
| 0.2379        | 4.0   | 2540 | 0.9110          | 0.7078   | 0.6281                   | 0.7078                   | 0.5874                | 0.7078                | 0.5910            | 0.7078            |
| 0.1511        | 5.0   | 3175 | 1.2270          | 0.6953   | 0.6168                   | 0.6953                   | 0.5963                | 0.6953                | 0.6011            | 0.6953            |
| 0.1074        | 6.0   | 3810 | 1.6106          | 0.7188   | 0.6470                   | 0.7188                   | 0.5859                | 0.7188                | 0.5875            | 0.7188            |
| 0.0935        | 7.0   | 4445 | 1.8533          | 0.7070   | 0.6266                   | 0.7070                   | 0.5861                | 0.7070                | 0.5895            | 0.7070            |
| 0.037         | 8.0   | 5080 | 2.0315          | 0.6875   | 0.6082                   | 0.6875                   | 0.5923                | 0.6875                | 0.5964            | 0.6875            |
| 0.0294        | 9.0   | 5715 | 2.0726          | 0.7078   | 0.6295                   | 0.7078                   | 0.5928                | 0.7078                | 0.5975            | 0.7078            |
| 0.0238        | 10.0  | 6350 | 2.1236          | 0.7086   | 0.6303                   | 0.7086                   | 0.5918                | 0.7086                | 0.5963            | 0.7086            |


### 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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-Multi_classification", "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-Multi\_classification
=======================================================


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: 0.5588
* Accuracy: 0.7266
* Macro Averaged Precision: 0.6830
* Micro Averaged Precision: 0.7266
* Macro Averaged Recall: 0.5652
* Micro Averaged Recall: 0.7266
* Macro Averaged F1: 0.5513
* Micro Averaged F1: 0.7266


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: 10


### 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: 10 ### 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: 10 ### Training results ### Framework versions


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