---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-downstream-build_rr
  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-downstream-build_rr

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: 0.8610
- Precision-macro: 0.6015
- Recall-macro: 0.5642
- Macro-f1: 0.5742
- Precision-micro: 0.7871
- Recall-micro: 0.7871
- Micro-f1: 0.7871
- Accuracy: 0.7871

## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision-macro | Recall-macro | Macro-f1 | Precision-micro | Recall-micro | Micro-f1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:--------:|
| No log        | 1.0   | 124  | 0.9703          | 0.5485          | 0.3447       | 0.3566   | 0.7155          | 0.7155       | 0.7155   | 0.7155   |
| No log        | 2.0   | 248  | 0.8005          | 0.5181          | 0.5222       | 0.5080   | 0.7353          | 0.7353       | 0.7353   | 0.7353   |
| No log        | 3.0   | 372  | 0.8156          | 0.5626          | 0.5322       | 0.5288   | 0.7454          | 0.7454       | 0.7454   | 0.7454   |
| No log        | 4.0   | 496  | 0.7056          | 0.5881          | 0.5197       | 0.5180   | 0.7704          | 0.7704       | 0.7704   | 0.7704   |
| 1.0549        | 5.0   | 620  | 0.7526          | 0.5878          | 0.5906       | 0.5775   | 0.7642          | 0.7642       | 0.7642   | 0.7642   |
| 1.0549        | 6.0   | 744  | 0.7094          | 0.6336          | 0.5395       | 0.5649   | 0.7812          | 0.7812       | 0.7812   | 0.7812   |
| 1.0549        | 7.0   | 868  | 0.7391          | 0.6475          | 0.5339       | 0.5535   | 0.7808          | 0.7808       | 0.7808   | 0.7808   |
| 1.0549        | 8.0   | 992  | 0.7354          | 0.6169          | 0.5756       | 0.5881   | 0.7930          | 0.7930       | 0.7930   | 0.7930   |
| 0.545         | 9.0   | 1116 | 0.8143          | 0.5951          | 0.5963       | 0.5928   | 0.7805          | 0.7805       | 0.7805   | 0.7805   |
| 0.545         | 10.0  | 1240 | 0.8352          | 0.6029          | 0.5915       | 0.5918   | 0.7794          | 0.7794       | 0.7794   | 0.7794   |
| 0.545         | 11.0  | 1364 | 0.8610          | 0.6015          | 0.5642       | 0.5742   | 0.7871          | 0.7871       | 0.7871   | 0.7871   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1