---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-aces
  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-large-aces

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5257
- Precision: 0.8561
- Recall: 0.8594
- F1: 0.8553
- Accuracy: 0.8594
- F1 Who: 0.8494
- F1 What: 0.8391
- F1 Where: 0.7558
- F1 How: 0.9208

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | F1 Who | F1 What | F1 Where | F1 How |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
| 0.4619        | 1.0   | 87   | 0.5447          | 0.8247    | 0.8416 | 0.8308 | 0.8416   | 0.8309 | 0.8188  | 0.6973   | 0.9244 |
| 0.4358        | 2.0   | 174  | 0.4662          | 0.8522    | 0.8571 | 0.8517 | 0.8571   | 0.8314 | 0.8446  | 0.7613   | 0.9238 |
| 0.3793        | 3.0   | 261  | 0.4892          | 0.8507    | 0.8622 | 0.8556 | 0.8622   | 0.8321 | 0.8418  | 0.7725   | 0.9280 |
| 0.2875        | 4.0   | 348  | 0.5034          | 0.8702    | 0.8641 | 0.8593 | 0.8641   | 0.8471 | 0.8441  | 0.7715   | 0.9225 |
| 0.1847        | 5.0   | 435  | 0.5257          | 0.8561    | 0.8594 | 0.8553 | 0.8594   | 0.8494 | 0.8391  | 0.7558   | 0.9208 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2