---
library_name: transformers
license: apache-2.0
base_model: albert/albert-xlarge-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: 786803fb46c971c9608429e8dab90069
  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. -->

# 786803fb46c971c9608429e8dab90069

This model is a fine-tuned version of [albert/albert-xlarge-v2](https://huggingface.co/albert/albert-xlarge-v2) on the nyu-mll/glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6935
- Data Size: 0.25
- Epoch Runtime: 63.3036
- Accuracy: 0.4943
- F1 Macro: 0.3308
- Rouge1: 0.4947
- Rouge2: 0.0
- Rougel: 0.4943
- Rougelsum: 0.4944

## 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     | 0.7887          | 0         | 4.3758        | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |
| No log        | 1     | 3273  | 0.6953          | 0.0078    | 6.4227        | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |
| 0.0118        | 2     | 6546  | 0.6927          | 0.0156    | 8.1931        | 0.5024   | 0.4157   | 0.5020 | 0.0    | 0.5019 | 0.5024    |
| 0.718         | 3     | 9819  | 0.6973          | 0.0312    | 11.9350       | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |
| 0.7175        | 4     | 13092 | 0.6931          | 0.0625    | 19.1269       | 0.5057   | 0.3359   | 0.5053 | 0.0    | 0.5057 | 0.5056    |
| 0.7213        | 5     | 16365 | 0.7066          | 0.125     | 33.6047       | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |
| 0.7133        | 6     | 19638 | 0.6935          | 0.25      | 63.3036       | 0.4943   | 0.3308   | 0.4947 | 0.0    | 0.4943 | 0.4944    |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.1