---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v1
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: 1a7d904346e7c4adfb1de13434e74811
  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. -->

# 1a7d904346e7c4adfb1de13434e74811

This model is a fine-tuned version of [albert/albert-base-v1](https://huggingface.co/albert/albert-base-v1) on the google/boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8393
- Data Size: 1.0
- Epoch Runtime: 11.5957
- Accuracy: 0.7365
- F1 Macro: 0.7092
- Rouge1: 0.7365
- Rouge2: 0.0
- Rougel: 0.7359
- Rougelsum: 0.7362

## 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    | 0.7204          | 0         | 1.6779        | 0.4779   | 0.4482   | 0.4779 | 0.0    | 0.4782 | 0.4773    |
| No log        | 1     | 294  | 0.7127          | 0.0078    | 3.0497        | 0.4605   | 0.4594   | 0.4602 | 0.0    | 0.4608 | 0.4602    |
| No log        | 2     | 588  | 0.6631          | 0.0156    | 1.8627        | 0.6213   | 0.3832   | 0.6213 | 0.0    | 0.6207 | 0.6210    |
| No log        | 3     | 882  | 0.6623          | 0.0312    | 2.0432        | 0.6222   | 0.4283   | 0.6222 | 0.0    | 0.6213 | 0.6225    |
| 0.0272        | 4     | 1176 | 0.6547          | 0.0625    | 2.3096        | 0.6219   | 0.3850   | 0.6219 | 0.0    | 0.6215 | 0.6216    |
| 0.0546        | 5     | 1470 | 0.6508          | 0.125     | 2.9116        | 0.6385   | 0.4884   | 0.6382 | 0.0    | 0.6382 | 0.6385    |
| 0.0898        | 6     | 1764 | 0.6159          | 0.25      | 4.1202        | 0.6615   | 0.5994   | 0.6615 | 0.0    | 0.6612 | 0.6615    |
| 0.5742        | 7     | 2058 | 0.5887          | 0.5       | 6.5760        | 0.6893   | 0.6685   | 0.6890 | 0.0    | 0.6893 | 0.6893    |
| 0.5151        | 8.0   | 2352 | 0.5455          | 1.0       | 11.4274       | 0.7230   | 0.6932   | 0.7237 | 0.0    | 0.7233 | 0.7233    |
| 0.4331        | 9.0   | 2646 | 0.5818          | 1.0       | 11.3375       | 0.7307   | 0.7003   | 0.7310 | 0.0    | 0.7304 | 0.7307    |
| 0.2983        | 10.0  | 2940 | 0.6985          | 1.0       | 11.3204       | 0.7374   | 0.7160   | 0.7381 | 0.0    | 0.7374 | 0.7374    |
| 0.2542        | 11.0  | 3234 | 0.7486          | 1.0       | 11.4927       | 0.7405   | 0.7218   | 0.7405 | 0.0    | 0.7405 | 0.7405    |
| 0.1905        | 12.0  | 3528 | 0.8393          | 1.0       | 11.5957       | 0.7365   | 0.7092   | 0.7365 | 0.0    | 0.7359 | 0.7362    |


### Framework versions

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