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---
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
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