---
base_model: distilbert/distilroberta-base
datasets:
- financial_phrasebank
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: my_miniroberta_model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: financial_phrasebank
      type: financial_phrasebank
      config: sentences_allagree
      split: train
      args: sentences_allagree
    metrics:
    - type: accuracy
      value: 0.9713024282560706
      name: Accuracy
---

<!-- 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. -->

# my_miniroberta_model

This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1663
- Accuracy: 0.9713

## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 227  | 0.2026          | 0.9338   |
| No log        | 2.0   | 454  | 0.1337          | 0.9669   |
| 0.2375        | 3.0   | 681  | 0.1639          | 0.9713   |
| 0.2375        | 4.0   | 908  | 0.1499          | 0.9735   |
| 0.0176        | 5.0   | 1135 | 0.1663          | 0.9713   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1