---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-papernew
  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. -->

# roberta-base-finetuned-papernew

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: 0.1716
- Precision: 0.7586
- Recall: 0.8056
- F1: 0.7814
- Accuracy: 0.9734

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 81   | 0.1846          | 0.6825    | 0.4845 | 0.5667 | 0.9497   |
| No log        | 2.0   | 162  | 0.1238          | 0.6952    | 0.6873 | 0.6912 | 0.9671   |
| No log        | 3.0   | 243  | 0.1032          | 0.7351    | 0.7662 | 0.7503 | 0.9711   |
| No log        | 4.0   | 324  | 0.1060          | 0.8169    | 0.7915 | 0.8040 | 0.9767   |
| No log        | 5.0   | 405  | 0.1144          | 0.7892    | 0.7803 | 0.7847 | 0.9743   |
| No log        | 6.0   | 486  | 0.1257          | 0.7378    | 0.8085 | 0.7715 | 0.9706   |
| 0.1393        | 7.0   | 567  | 0.1392          | 0.7379    | 0.8169 | 0.7754 | 0.9705   |
| 0.1393        | 8.0   | 648  | 0.1333          | 0.7304    | 0.7859 | 0.7571 | 0.9709   |
| 0.1393        | 9.0   | 729  | 0.1419          | 0.7539    | 0.8113 | 0.7815 | 0.9725   |
| 0.1393        | 10.0  | 810  | 0.1570          | 0.7385    | 0.8113 | 0.7732 | 0.9712   |
| 0.1393        | 11.0  | 891  | 0.1645          | 0.7318    | 0.7915 | 0.7605 | 0.9704   |
| 0.1393        | 12.0  | 972  | 0.1586          | 0.7506    | 0.8141 | 0.7811 | 0.9733   |
| 0.0126        | 13.0  | 1053 | 0.1589          | 0.7474    | 0.8169 | 0.7806 | 0.9747   |
| 0.0126        | 14.0  | 1134 | 0.1616          | 0.7527    | 0.7972 | 0.7743 | 0.9737   |
| 0.0126        | 15.0  | 1215 | 0.1642          | 0.7410    | 0.8141 | 0.7758 | 0.9736   |
| 0.0126        | 16.0  | 1296 | 0.1725          | 0.7429    | 0.8056 | 0.7730 | 0.9726   |
| 0.0126        | 17.0  | 1377 | 0.1686          | 0.7487    | 0.8056 | 0.7761 | 0.9737   |
| 0.0126        | 18.0  | 1458 | 0.1657          | 0.7533    | 0.8    | 0.7760 | 0.9740   |
| 0.0034        | 19.0  | 1539 | 0.1716          | 0.7493    | 0.8085 | 0.7778 | 0.9738   |
| 0.0034        | 20.0  | 1620 | 0.1716          | 0.7586    | 0.8056 | 0.7814 | 0.9734   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2