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# extended_distilBERT-finetuned-resumes-sections

This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0321
- F1: 0.9735
- Roc Auc: 0.9850
- Accuracy: 0.9715

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.0283        | 1.0   | 2213  | 0.0247          | 0.9610 | 0.9763  | 0.9539   |
| 0.0153        | 2.0   | 4426  | 0.0223          | 0.9634 | 0.9789  | 0.9593   |
| 0.01          | 3.0   | 6639  | 0.0199          | 0.9702 | 0.9835  | 0.9675   |
| 0.0073        | 4.0   | 8852  | 0.0218          | 0.9710 | 0.9838  | 0.9690   |
| 0.0063        | 5.0   | 11065 | 0.0244          | 0.9706 | 0.9835  | 0.9684   |
| 0.0037        | 6.0   | 13278 | 0.0251          | 0.9700 | 0.9833  | 0.9684   |
| 0.004         | 7.0   | 15491 | 0.0273          | 0.9712 | 0.9837  | 0.9693   |
| 0.003         | 8.0   | 17704 | 0.0266          | 0.9719 | 0.9841  | 0.9695   |
| 0.0027        | 9.0   | 19917 | 0.0294          | 0.9697 | 0.9831  | 0.9679   |
| 0.0014        | 10.0  | 22130 | 0.0275          | 0.9714 | 0.9844  | 0.9690   |
| 0.0016        | 11.0  | 24343 | 0.0299          | 0.9714 | 0.9839  | 0.9697   |
| 0.0013        | 12.0  | 26556 | 0.0297          | 0.9719 | 0.9852  | 0.9697   |
| 0.0006        | 13.0  | 28769 | 0.0312          | 0.9711 | 0.9843  | 0.9697   |
| 0.0004        | 14.0  | 30982 | 0.0305          | 0.9731 | 0.9849  | 0.9720   |
| 0.0004        | 15.0  | 33195 | 0.0312          | 0.9723 | 0.9845  | 0.9704   |
| 0.0005        | 16.0  | 35408 | 0.0331          | 0.9716 | 0.9843  | 0.9697   |
| 0.0006        | 17.0  | 37621 | 0.0321          | 0.9735 | 0.9850  | 0.9715   |
| 0.0004        | 18.0  | 39834 | 0.0322          | 0.9731 | 0.9850  | 0.9711   |
| 0.0003        | 19.0  | 42047 | 0.0332          | 0.9722 | 0.9847  | 0.9706   |
| 0.0004        | 20.0  | 44260 | 0.0334          | 0.9720 | 0.9846  | 0.9704   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

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

extended\_distilBERT-finetuned-resumes-sections
===============================================


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


* Loss: 0.0321
* F1: 0.9735
* Roc Auc: 0.9850
* Accuracy: 0.9715


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1