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

# Gregariousness_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: 0.6050
- Accuracy: 0.6794
- Precision: 0.6869
- Recall: 0.5980
- F1: 0.6394
- Auc: 0.6756

## 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  | 0.6922          | 0.4753   | 0.4753    | 1.0    | 0.6443 | 0.5    |
| No log        | 2.0   | 268  | 0.6288          | 0.6207   | 0.7696    | 0.2882 | 0.4194 | 0.6050 |
| No log        | 3.0   | 402  | 0.6050          | 0.6794   | 0.6869    | 0.5980 | 0.6394 | 0.6756 |


### Framework versions

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