---
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: 0.8272
- Precision-macro: 0.6089
- Recall-macro: 0.5868
- Macro-f1: 0.5926
- Precision-micro: 0.7798
- Recall-micro: 0.7798
- Micro-f1: 0.7798
- Accuracy: 0.7798

## 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: 4
- eval_batch_size: 4
- 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   | 62   | 1.1797          | 0.3651          | 0.2425       | 0.2406   | 0.6509          | 0.6509       | 0.6509   | 0.6509   |
| No log        | 2.0   | 124  | 0.8354          | 0.5350          | 0.5291       | 0.5255   | 0.7350          | 0.7350       | 0.7350   | 0.7350   |
| No log        | 3.0   | 186  | 0.8058          | 0.5559          | 0.5382       | 0.5366   | 0.7343          | 0.7343       | 0.7343   | 0.7343   |
| No log        | 4.0   | 248  | 0.7718          | 0.6246          | 0.5201       | 0.5300   | 0.7503          | 0.7503       | 0.7503   | 0.7503   |
| No log        | 5.0   | 310  | 0.7307          | 0.5890          | 0.5463       | 0.5579   | 0.7642          | 0.7642       | 0.7642   | 0.7642   |
| No log        | 6.0   | 372  | 0.7099          | 0.6076          | 0.5431       | 0.5481   | 0.7746          | 0.7746       | 0.7746   | 0.7746   |
| No log        | 7.0   | 434  | 0.7072          | 0.6090          | 0.5126       | 0.5261   | 0.7812          | 0.7812       | 0.7812   | 0.7812   |
| No log        | 8.0   | 496  | 0.6919          | 0.6321          | 0.5471       | 0.5676   | 0.7826          | 0.7826       | 0.7826   | 0.7826   |
| 0.8758        | 9.0   | 558  | 0.7503          | 0.5666          | 0.5818       | 0.5696   | 0.7735          | 0.7735       | 0.7735   | 0.7735   |
| 0.8758        | 10.0  | 620  | 0.7512          | 0.6054          | 0.5656       | 0.5755   | 0.7784          | 0.7784       | 0.7784   | 0.7784   |
| 0.8758        | 11.0  | 682  | 0.7656          | 0.6086          | 0.5835       | 0.5913   | 0.7829          | 0.7829       | 0.7829   | 0.7829   |
| 0.8758        | 12.0  | 744  | 0.7861          | 0.5972          | 0.5885       | 0.5843   | 0.7739          | 0.7739       | 0.7739   | 0.7739   |
| 0.8758        | 13.0  | 806  | 0.8239          | 0.5975          | 0.5749       | 0.5701   | 0.7780          | 0.7780       | 0.7780   | 0.7780   |
| 0.8758        | 14.0  | 868  | 0.8272          | 0.6089          | 0.5868       | 0.5926   | 0.7798          | 0.7798       | 0.7798   | 0.7798   |


### Framework versions

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