---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: classify-articles
  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. -->

# classify-articles

This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co./albert/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3819
- Accuracy: 0.9070
- F1: 0.9061
- Precision: 0.9126
- Recall: 0.9070
- Accuracy Label Economy: 0.9429
- Accuracy Label Politics: 0.9574
- Accuracy Label Science: 0.9362
- Accuracy Label Sports: 0.96
- Accuracy Label Technology: 0.6944

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Accuracy Label Economy | Accuracy Label Politics | Accuracy Label Science | Accuracy Label Sports | Accuracy Label Technology |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:|:-----------------------:|:----------------------:|:---------------------:|:-------------------------:|
| 1.3703        | 1.3072 | 100  | 1.3775          | 0.4930   | 0.4238 | 0.6100    | 0.4930 | 0.8                    | 0.0213                  | 0.7021                 | 0.72                  | 0.2222                    |
| 0.4329        | 2.6144 | 200  | 0.4495          | 0.8977   | 0.9004 | 0.9134    | 0.8977 | 0.9429                 | 0.8936                  | 0.9149                 | 0.96                  | 0.75                      |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1