metadata
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-topic-classification
results: []
datasets:
- valurank/Topic_Classification
language:
- en
metrics:
- f1
distilroberta-topic-classification
This model is a fine-tuned version of distilroberta-topic-base on a dataset of headlines. It achieves the following results on the evaluation set:
- Loss: 2.235735
- F1: 0.756
Training and evaluation data
The following data sources were used:
- 22k News articles classified into 120 different topics from Hugging face
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 2.3851 | 1.0 | 561 | 2.3445 | 0.6495 |
| 2.1441 | 2.0 | 1122 | 2.1980 | 0.7019 |
| 1.9992 | 3.0 | 1683 | 2.1720 | 0.7189 |
| 1.8384 | 4.0 | 2244 | 2.1425 | 0.7403 |
| 1.7468 | 5.0 | 2805 | 2.1666 | 0.7453 |
| 1.6360 | 6.0 | 3366 | 2.1779 | 0.7456 |
| 1.5935 | 7.0 | 3927 | 2.2003 | 0.7555 |
| 1.5460 | 8.0 | 4488 | 2.2157 | 0.7575 |
| 1.5510 | 9.0 | 5049 | 2.2300 | 0.7536 |
| 1.5097 | 10.0 | 5610 | 2.2357 | 0.7547 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0