---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: aces-roberta-base-13
  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. -->

# aces-roberta-base-13

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: 0.5171
- Precision: 0.8348
- Recall: 0.8531
- F1: 0.8399
- Accuracy: 0.8531
- F1 Who: 0.9134
- F1 What: 0.8505
- F1 Where: 0.8444
- F1 How: 0.9391

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | F1 Who | F1 What | F1 Where | F1 How |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
| 1.2251        | 1.0   | 50   | 1.1108          | 0.6219    | 0.6941 | 0.6168 | 0.6941   | 0.0625 | 0.6856  | 0.5926   | 0.8138 |
| 0.6932        | 2.0   | 100  | 0.7015          | 0.7448    | 0.8031 | 0.7639 | 0.8031   | 0.8730 | 0.7932  | 0.8054   | 0.9293 |
| 0.5636        | 3.0   | 150  | 0.6059          | 0.8028    | 0.8289 | 0.8032 | 0.8289   | 0.8819 | 0.8095  | 0.8186   | 0.9346 |
| 0.4894        | 4.0   | 200  | 0.5492          | 0.8251    | 0.8499 | 0.8314 | 0.8499   | 0.9077 | 0.8402  | 0.8340   | 0.9393 |
| 0.4381        | 5.0   | 250  | 0.5289          | 0.8237    | 0.8523 | 0.8353 | 0.8523   | 0.9219 | 0.8497  | 0.8559   | 0.9438 |
| 0.4611        | 6.0   | 300  | 0.5233          | 0.8217    | 0.8507 | 0.8345 | 0.8507   | 0.9219 | 0.8346  | 0.8267   | 0.9436 |
| 0.3671        | 7.0   | 350  | 0.5268          | 0.8383    | 0.8507 | 0.8360 | 0.8507   | 0.9206 | 0.8485  | 0.8393   | 0.9395 |
| 0.3278        | 8.0   | 400  | 0.5278          | 0.8370    | 0.8507 | 0.8369 | 0.8507   | 0.9147 | 0.8448  | 0.8444   | 0.9348 |
| 0.3727        | 9.0   | 450  | 0.5170          | 0.8339    | 0.8547 | 0.8405 | 0.8547   | 0.9134 | 0.8549  | 0.8407   | 0.9423 |
| 0.372         | 10.0  | 500  | 0.5171          | 0.8348    | 0.8531 | 0.8399 | 0.8531   | 0.9134 | 0.8505  | 0.8444   | 0.9391 |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.15.0
- Tokenizers 0.13.3