Edit model card

distilbert-base-uncased-finetuned-tagesschau-subcategories

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7723
  • Accuracy: 0.7267

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.4 30 1.3433 0.5667
No log 0.8 60 1.0861 0.6933
No log 1.2 90 0.9395 0.7067
No log 1.6 120 0.8647 0.68
No log 2.0 150 0.8018 0.72
No log 2.4 180 0.7723 0.7267
No log 2.8 210 0.7616 0.72
No log 3.2 240 0.7348 0.7067
No log 3.6 270 0.7747 0.72

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2
Downloads last month
11
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for tillschwoerer/distilbert-base-uncased-finetuned-tagesschau-subcategories

Finetuned
(6759)
this model