---
library_name: transformers
license: apache-2.0
base_model: albert/albert-xxlarge-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 27afceb9392c23d1674a5a2901ca7d34
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 27afceb9392c23d1674a5a2901ca7d34

This model is a fine-tuned version of [albert/albert-xxlarge-v2](https://huggingface.co/albert/albert-xxlarge-v2) on the dair-ai/emotion [split] dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3531
- Data Size: 1.0
- Epoch Runtime: 41.1003
- Accuracy: 0.9088
- F1 Macro: 0.8659

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|
| No log        | 0     | 0    | 2.2406          | 0         | 1.9154        | 0.0938   | 0.0641   |
| No log        | 1     | 500  | 1.7164          | 0.0078    | 2.2981        | 0.2359   | 0.1245   |
| No log        | 2     | 1000 | 1.7238          | 0.0156    | 2.6170        | 0.2747   | 0.1135   |
| No log        | 3     | 1500 | 1.5864          | 0.0312    | 3.4158        | 0.3508   | 0.0944   |
| No log        | 4     | 2000 | 1.5906          | 0.0625    | 4.7034        | 0.3054   | 0.1008   |
| 0.0883        | 5     | 2500 | 1.4474          | 0.125     | 7.0518        | 0.5050   | 0.2052   |
| 1.3713        | 6     | 3000 | 1.3480          | 0.25      | 12.0165       | 0.5469   | 0.2198   |
| 0.1954        | 7     | 3500 | 1.0987          | 0.5       | 21.9685       | 0.6144   | 0.2500   |
| 0.4637        | 8.0   | 4000 | 0.4034          | 1.0       | 41.8969       | 0.8831   | 0.7363   |
| 0.4674        | 9.0   | 4500 | 0.4547          | 1.0       | 41.4539       | 0.8866   | 0.7876   |
| 0.3519        | 10.0  | 5000 | 0.3257          | 1.0       | 41.3873       | 0.9057   | 0.8543   |
| 0.2821        | 11.0  | 5500 | 0.2998          | 1.0       | 41.3973       | 0.9113   | 0.8625   |
| 0.2597        | 12.0  | 6000 | 0.2644          | 1.0       | 41.4418       | 0.9148   | 0.8722   |
| 0.2649        | 13.0  | 6500 | 0.3314          | 1.0       | 41.2494       | 0.9158   | 0.8698   |
| 0.255         | 14.0  | 7000 | 0.2704          | 1.0       | 41.4583       | 0.9113   | 0.8641   |
| 0.1864        | 15.0  | 7500 | 0.3561          | 1.0       | 41.3138       | 0.9178   | 0.8721   |
| 0.2955        | 16.0  | 8000 | 0.3531          | 1.0       | 41.1003       | 0.9088   | 0.8659   |


### Framework versions

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