metadata
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: []
classify-articles
This model is a fine-tuned version of 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