transformers safetensors roberta multiple-choice generated_from_trainer base_model:roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-riddle-finetuned_new
  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-riddle-finetuned_new

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: 0.3741
- Accuracy: 0.8250

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 12   | 1.3036          | 0.4500   |
| No log        | 2.0   | 24   | 1.1112          | 0.4750   |
| No log        | 3.0   | 36   | 1.0265          | 0.4500   |
| No log        | 4.0   | 48   | 1.1130          | 0.6000   |
| No log        | 5.0   | 60   | 0.8603          | 0.625    |
| No log        | 6.0   | 72   | 0.8099          | 0.7750   |
| No log        | 7.0   | 84   | 0.8668          | 0.7250   |
| No log        | 8.0   | 96   | 0.7217          | 0.8000   |
| No log        | 9.0   | 108  | 0.7143          | 0.8250   |
| No log        | 10.0  | 120  | 0.6371          | 0.8250   |
| No log        | 11.0  | 132  | 0.6327          | 0.7250   |
| No log        | 12.0  | 144  | 0.5974          | 0.75     |
| No log        | 13.0  | 156  | 0.5160          | 0.8000   |
| No log        | 14.0  | 168  | 0.5336          | 0.75     |
| No log        | 15.0  | 180  | 0.5201          | 0.8000   |
| No log        | 16.0  | 192  | 0.4121          | 0.8250   |
| No log        | 17.0  | 204  | 0.4145          | 0.8000   |
| No log        | 18.0  | 216  | 0.4475          | 0.875    |
| No log        | 19.0  | 228  | 0.4147          | 0.8250   |
| No log        | 20.0  | 240  | 0.3818          | 0.7750   |
| No log        | 21.0  | 252  | 0.4136          | 0.75     |
| No log        | 22.0  | 264  | 0.4364          | 0.75     |
| No log        | 23.0  | 276  | 0.4180          | 0.7250   |
| No log        | 24.0  | 288  | 0.4145          | 0.75     |
| No log        | 25.0  | 300  | 0.4141          | 0.8000   |
| No log        | 26.0  | 312  | 0.3948          | 0.8000   |
| No log        | 27.0  | 324  | 0.3930          | 0.8250   |
| No log        | 28.0  | 336  | 0.3851          | 0.8250   |
| No log        | 29.0  | 348  | 0.3745          | 0.8250   |
| No log        | 30.0  | 360  | 0.3741          | 0.8250   |


### Framework versions

- Transformers 4.37.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
