---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-Questions-goodareas-eval_FeedbackESConv5pp_CARE10pp-sweeps-current
  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. -->

# roberta-Questions-goodareas-eval_FeedbackESConv5pp_CARE10pp-sweeps-current

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3192
- Accuracy: 0.7805
- Precision: 0.6813
- Recall: 0.8185
- F1: 0.7436

## 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: 3.0384066791847988e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5249        | 1.0   | 165  | 0.2628          | 0.6110   | 0.0       | 0.0    | 0.0    |
| 0.4371        | 2.0   | 330  | 0.3133          | 0.7843   | 0.6517    | 0.9571 | 0.7754 |
| 0.3969        | 3.0   | 495  | 0.2745          | 0.6226   | 0.6364    | 0.0693 | 0.125  |
| 0.3765        | 4.0   | 660  | 0.3192          | 0.7805   | 0.6813    | 0.8185 | 0.7436 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0