---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- silicone
metrics:
- accuracy
model-index:
- name: distilroberta-base
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: silicone
      type: silicone
      config: swda
      split: test
      args: swda
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7111274871039057
---

<!-- 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. -->

# distilroberta-base

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the silicone dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9647
- Accuracy: 0.7111
- Micro-precision: 0.7111
- Micro-recall: 0.7111
- Micro-f1: 0.7111
- Macro-precision: 0.3228
- Macro-recall: 0.2866
- Macro-f1: 0.2824
- Weighted-precision: 0.6683
- Weighted-recall: 0.7111
- Weighted-f1: 0.6768

## 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: 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 0.9578        | 1.0   | 2980 | 0.9647          | 0.7111   | 0.7111          | 0.7111       | 0.7111   | 0.3228          | 0.2866       | 0.2824   | 0.6683             | 0.7111          | 0.6768      |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2