metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- dstefa/New_York_Times_Topics
metrics:
- accuracy
model-index:
- name: DistilBERT base classify news topics - Devinit
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: New York Times Topics
type: dstefa/New_York_Times_Topics
metrics:
- name: Accuracy
type: accuracy
value: 0.913482481060606
widget:
- text: 'Insurers: Costs Would Skyrocket Under House Health Bill.'
DistilBERT base classify news topics - Devinit
This model is a fine-tuned version of distilbert-base-uncased on the New York Times Topics dataset. It achieves the following results on the evaluation set:
- Loss: 0.2871
- Accuracy: 0.9135
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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.386 | 1.0 | 1340 | 0.3275 | 0.8921 |
| 0.2833 | 2.0 | 2680 | 0.2840 | 0.9033 |
| 0.2411 | 3.0 | 4020 | 0.2694 | 0.9102 |
| 0.2069 | 4.0 | 5360 | 0.2665 | 0.9114 |
| 0.1796 | 5.0 | 6700 | 0.2657 | 0.9128 |
| 0.1636 | 6.0 | 8040 | 0.2674 | 0.9142 |
| 0.144 | 7.0 | 9380 | 0.2761 | 0.9129 |
| 0.1277 | 8.0 | 10720 | 0.2820 | 0.9125 |
| 0.1201 | 9.0 | 12060 | 0.2853 | 0.9136 |
| 0.1104 | 10.0 | 13400 | 0.2871 | 0.9135 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0