---
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: absa-train-service-roberta-large
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cunho2803032003/absa-1721959498.2993438/runs/tad25dun)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cunho2803032003/absa-1721959940.7872202/runs/bsprskdy)
# absa-train-service-roberta-large

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8683
- Accuracy: 0.7424
- Precision: 0.7345
- Recall: 0.7367
- F1: 0.7302

## 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: 0.0002
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.2255        | 1.0   | 469  | 2.0677          | 0.3296   | 0.1937    | 0.3250 | 0.2297 |
| 1.8236        | 2.0   | 938  | 1.7061          | 0.504    | 0.5413    | 0.4914 | 0.4567 |
| 1.5384        | 3.0   | 1407 | 1.4381          | 0.552    | 0.5944    | 0.5549 | 0.5196 |
| 1.4301        | 4.0   | 1876 | 1.3316          | 0.5984   | 0.6000    | 0.5990 | 0.5618 |
| 1.3776        | 5.0   | 2345 | 1.1645          | 0.6576   | 0.6817    | 0.6491 | 0.6332 |
| 1.2078        | 6.0   | 2814 | 1.0967          | 0.6448   | 0.7035    | 0.6348 | 0.6110 |
| 1.2535        | 7.0   | 3283 | 1.0565          | 0.7008   | 0.7467    | 0.6967 | 0.7066 |
| 1.2921        | 8.0   | 3752 | 1.0049          | 0.6976   | 0.7013    | 0.6884 | 0.6813 |
| 1.178         | 9.0   | 4221 | 1.0438          | 0.648    | 0.7746    | 0.6423 | 0.6387 |
| 1.2324        | 10.0  | 4690 | 1.0203          | 0.6896   | 0.7096    | 0.6831 | 0.6704 |
| 1.1899        | 11.0  | 5159 | 1.0193          | 0.6864   | 0.7391    | 0.6819 | 0.6834 |
| 1.1515        | 12.0  | 5628 | 0.9722          | 0.6944   | 0.7164    | 0.6924 | 0.6860 |
| 1.1604        | 13.0  | 6097 | 0.9372          | 0.7312   | 0.7543    | 0.7311 | 0.7259 |
| 1.1229        | 14.0  | 6566 | 0.9265          | 0.72     | 0.7278    | 0.7139 | 0.7147 |
| 1.1459        | 15.0  | 7035 | 0.8896          | 0.7376   | 0.7264    | 0.7323 | 0.7183 |
| 1.1281        | 16.0  | 7504 | 0.9074          | 0.7152   | 0.7107    | 0.7087 | 0.7012 |
| 1.1794        | 17.0  | 7973 | 0.8914          | 0.7424   | 0.7293    | 0.7354 | 0.7266 |
| 1.1101        | 18.0  | 8442 | 0.8707          | 0.7216   | 0.7161    | 0.7141 | 0.7059 |
| 1.1215        | 19.0  | 8911 | 0.8656          | 0.7408   | 0.7322    | 0.7348 | 0.7274 |
| 1.0483        | 20.0  | 9380 | 0.8683          | 0.7424   | 0.7345    | 0.7367 | 0.7302 |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1