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# roberta-base-finetuned-recruitment-exp

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1044
- Precision: 0.7320
- Recall: 0.8560
- F1: 0.7892
- Accuracy: 0.9713

## 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   | 0.4051          | 0.25      | 0.0061 | 0.0119 | 0.8984   |
| No log        | 2.0   | 34   | 0.2450          | 0.4040    | 0.3732 | 0.3880 | 0.9280   |
| No log        | 3.0   | 51   | 0.1481          | 0.5385    | 0.6663 | 0.5956 | 0.9555   |
| No log        | 4.0   | 68   | 0.1269          | 0.6295    | 0.7961 | 0.7031 | 0.964    |
| No log        | 5.0   | 85   | 0.1101          | 0.6639    | 0.8235 | 0.7352 | 0.9679   |
| No log        | 6.0   | 102  | 0.1116          | 0.7287    | 0.7819 | 0.7544 | 0.9701   |
| No log        | 7.0   | 119  | 0.1160          | 0.7026    | 0.8266 | 0.7596 | 0.9684   |
| No log        | 8.0   | 136  | 0.1071          | 0.7442    | 0.8499 | 0.7936 | 0.9717   |
| No log        | 9.0   | 153  | 0.1044          | 0.7320    | 0.8560 | 0.7892 | 0.9713   |
| No log        | 10.0  | 170  | 0.1081          | 0.7532    | 0.8448 | 0.7964 | 0.9722   |


### Framework versions

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

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

roberta-base-finetuned-recruitment-exp
======================================


This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1044
* Precision: 0.7320
* Recall: 0.8560
* F1: 0.7892
* Accuracy: 0.9713


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 #roberta #token-classification #generated_from_trainer #license-mit #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