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---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: IngroupLoyalty_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. -->
# IngroupLoyalty_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: 0.6927
- Accuracy: 0.6690
- Precision: 0.6752
- Recall: 0.7010
- F1: 0.6879
- Auc: 0.6676
## 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 | 127 | 0.6813 | 0.6601 | 0.7333 | 0.5448 | 0.6251 | 0.6649 |
| No log | 2.0 | 254 | 0.6369 | 0.6868 | 0.6683 | 0.7905 | 0.7243 | 0.6824 |
| No log | 3.0 | 381 | 0.6927 | 0.6690 | 0.6752 | 0.7010 | 0.6879 | 0.6676 |
### Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
|