---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- recall
model-index:
- name: study-dictionary-roberta-base
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# study-dictionary-roberta-base

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: 0.0011
- F1: 1.0
- Roc Auc: 1.0
- Accuracy: 1.0
- Recall: 1.0

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:------:|
| 0.3342        | 1.0   | 778   | 0.1192          | 0.0    | 0.5     | 0.0      | 0.0    |
| 0.1099        | 2.0   | 1556  | 0.1040          | 0.0    | 0.5     | 0.0      | 0.0    |
| 0.0892        | 3.0   | 2334  | 0.0465          | 0.6835 | 0.7644  | 0.5479   | 0.5293 |
| 0.0345        | 4.0   | 3112  | 0.0240          | 0.9147 | 0.9241  | 0.8817   | 0.8485 |
| 0.025         | 5.0   | 3890  | 0.0152          | 0.9594 | 0.9650  | 0.9493   | 0.9303 |
| 0.0144        | 6.0   | 4668  | 0.0114          | 0.9735 | 0.9811  | 0.9671   | 0.9625 |
| 0.0118        | 7.0   | 5446  | 0.0082          | 0.9779 | 0.9848  | 0.9717   | 0.9700 |
| 0.0081        | 8.0   | 6224  | 0.0057          | 0.9873 | 0.9887  | 0.9839   | 0.9774 |
| 0.0065        | 9.0   | 7002  | 0.0052          | 0.9839 | 0.9860  | 0.9848   | 0.9720 |
| 0.0054        | 10.0  | 7780  | 0.0039          | 0.9895 | 0.9904  | 0.9888   | 0.9809 |
| 0.0041        | 11.0  | 8558  | 0.0030          | 0.9942 | 0.9949  | 0.9925   | 0.9899 |
| 0.0036        | 12.0  | 9336  | 0.0026          | 0.9936 | 0.9940  | 0.9942   | 0.9881 |
| 0.0027        | 13.0  | 10114 | 0.0023          | 0.9956 | 0.9964  | 0.9958   | 0.9927 |
| 0.0023        | 14.0  | 10892 | 0.0018          | 0.9985 | 0.9986  | 0.9972   | 0.9972 |
| 0.0021        | 15.0  | 11670 | 0.0017          | 0.9985 | 0.9994  | 0.9974   | 0.9988 |
| 0.0018        | 16.0  | 12448 | 0.0015          | 0.9985 | 0.9992  | 0.9979   | 0.9985 |
| 0.0014        | 17.0  | 13226 | 0.0012          | 0.9997 | 0.9998  | 0.9994   | 0.9995 |
| 0.0013        | 18.0  | 14004 | 0.0011          | 1.0    | 1.0     | 1.0      | 1.0    |
| 0.0012        | 19.0  | 14782 | 0.0010          | 1.0    | 1.0     | 1.0      | 1.0    |
| 0.0012        | 20.0  | 15560 | 0.0010          | 1.0    | 1.0     | 1.0      | 1.0    |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3