---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: insuff_supported_arguments
  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. -->

# insuff_supported_arguments

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Negative: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0}
- Positive: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0}
- Accuracy: 1.0
- Macro avg: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}
- Weighted avg: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Negative                                                               | Positive                                                              | Accuracy | Macro avg                                                              | Weighted avg                                                           |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------:|:---------------------------------------------------------------------:|:--------:|:----------------------------------------------------------------------:|:----------------------------------------------------------------------:|
| 0.0025        | 1.0   | 4628  | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0} | 1.0      | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} |
| 0.0           | 2.0   | 9256  | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0} | 1.0      | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} |
| 0.0           | 3.0   | 13884 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0} | 1.0      | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0} |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2