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# distilbert-base-uncased-finetuned-recruitment-exp

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: 0.1742
- Precision: 0.6204
- Recall: 0.6855
- F1: 0.6513
- Accuracy: 0.9561

## 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: 5e-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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 17   | 0.5203          | 0.0       | 0.0    | 0.0    | 0.8991   |
| No log        | 2.0   | 34   | 0.3797          | 0.2979    | 0.0277 | 0.0507 | 0.9030   |
| No log        | 3.0   | 51   | 0.2983          | 0.3171    | 0.4194 | 0.3612 | 0.9222   |
| No log        | 4.0   | 68   | 0.2321          | 0.4219    | 0.4916 | 0.4541 | 0.9375   |
| No log        | 5.0   | 85   | 0.2100          | 0.5076    | 0.5262 | 0.5168 | 0.9453   |
| No log        | 6.0   | 102  | 0.1899          | 0.5174    | 0.5885 | 0.5507 | 0.9506   |
| No log        | 7.0   | 119  | 0.1775          | 0.5395    | 0.6350 | 0.5834 | 0.9509   |
| No log        | 8.0   | 136  | 0.1817          | 0.6282    | 0.6617 | 0.6445 | 0.9550   |
| No log        | 9.0   | 153  | 0.1775          | 0.6262    | 0.6726 | 0.6485 | 0.9558   |
| No log        | 10.0  | 170  | 0.1742          | 0.6204    | 0.6855 | 0.6513 | 0.9561   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

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

distilbert-base-uncased-finetuned-recruitment-exp
=================================================


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: 0.1742
* Precision: 0.6204
* Recall: 0.6855
* F1: 0.6513
* Accuracy: 0.9561


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: 5e-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 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 5e-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 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3