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# roberta-base-finetuned-recruitment-eval-2

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: 0.1002
- Precision: 0.8023
- Recall: 0.8531
- F1: 0.8269
- Accuracy: 0.9760

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 15   | 0.3547          | 0.0       | 0.0    | 0.0    | 0.9127   |
| No log        | 2.0   | 30   | 0.2395          | 0.3442    | 0.2978 | 0.3194 | 0.9305   |
| No log        | 3.0   | 45   | 0.1640          | 0.5315    | 0.6253 | 0.5746 | 0.9576   |
| No log        | 4.0   | 60   | 0.1231          | 0.6518    | 0.7871 | 0.7131 | 0.9606   |
| No log        | 5.0   | 75   | 0.1076          | 0.7409    | 0.8208 | 0.7788 | 0.9708   |
| No log        | 6.0   | 90   | 0.1220          | 0.6817    | 0.8342 | 0.7503 | 0.9658   |
| No log        | 7.0   | 105  | 0.1030          | 0.7850    | 0.8167 | 0.8005 | 0.9757   |
| No log        | 8.0   | 120  | 0.1053          | 0.7769    | 0.8167 | 0.7963 | 0.9745   |
| No log        | 9.0   | 135  | 0.1002          | 0.8023    | 0.8531 | 0.8269 | 0.9760   |
| No log        | 10.0  | 150  | 0.1100          | 0.7689    | 0.8477 | 0.8064 | 0.9724   |
| No log        | 11.0  | 165  | 0.1061          | 0.7757    | 0.8531 | 0.8126 | 0.9731   |
| No log        | 12.0  | 180  | 0.1081          | 0.7748    | 0.8531 | 0.8121 | 0.9734   |
| No log        | 13.0  | 195  | 0.1095          | 0.7761    | 0.8504 | 0.8116 | 0.9737   |
| No log        | 14.0  | 210  | 0.1124          | 0.7800    | 0.8504 | 0.8137 | 0.9743   |
| No log        | 15.0  | 225  | 0.1117          | 0.7800    | 0.8504 | 0.8137 | 0.9746   |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.1+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-eval-2", "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-eval-2
=========================================


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


* Loss: 0.1002
* Precision: 0.8023
* Recall: 0.8531
* F1: 0.8269
* Accuracy: 0.9760


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: 15


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 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: 15 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+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: 15 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3