---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Consc_binary
  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. -->

# Consc_binary

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6987
- Accuracy: 0.7363
- Precision: 0.7160
- Recall: 0.7790
- F1: 0.7462
- Auc: 0.7365

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log        | 1.0   | 134  | 0.5583          | 0.7251   | 0.6937    | 0.8015 | 0.7437 | 0.7254 |
| No log        | 2.0   | 268  | 0.6184          | 0.7409   | 0.7199    | 0.7846 | 0.7509 | 0.7411 |
| No log        | 3.0   | 402  | 0.6987          | 0.7363   | 0.7160    | 0.7790 | 0.7462 | 0.7365 |


### Framework versions

- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1