---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: cc522f2f2219b6467260677999c26a6b
  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. -->

# cc522f2f2219b6467260677999c26a6b

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the nyu-mll/glue [qqp] dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8585
- Data Size: 0.25
- Epoch Runtime: 445.1982
- Accuracy: 0.3680
- F1 Macro: 0.2690
- Rouge1: 0.3682
- Rouge2: 0.0
- Rougel: 0.3681
- Rougelsum: 0.3683

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:|
| No log        | 0     | 0     | 0.6540          | 0         | 52.7420       | 0.6320   | 0.3872   | 0.6318 | 0.0    | 0.6319 | 0.6317    |
| 0.6451        | 1     | 11370 | 0.5819          | 0.0078    | 66.7814       | 0.7987   | 0.7821   | 0.7988 | 0.0    | 0.7987 | 0.7987    |
| 0.5842        | 2     | 22740 | 0.5633          | 0.0156    | 78.9004       | 0.7674   | 0.7504   | 0.7674 | 0.0    | 0.7674 | 0.7673    |
| 0.6603        | 3     | 34110 | 0.6254          | 0.0312    | 103.6159      | 0.6527   | 0.6307   | 0.6527 | 0.0    | 0.6527 | 0.6526    |
| 0.6767        | 4     | 45480 | 0.6581          | 0.0625    | 151.4573      | 0.6320   | 0.3872   | 0.6318 | 0.0    | 0.6319 | 0.6317    |
| 0.6693        | 5     | 56850 | 0.6608          | 0.125     | 250.6893      | 0.6320   | 0.3872   | 0.6318 | 0.0    | 0.6319 | 0.6317    |
| 0.6585        | 6     | 68220 | 0.8585          | 0.25      | 445.1982      | 0.3680   | 0.2690   | 0.3682 | 0.0    | 0.3681 | 0.3683    |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1