metadata
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: requirements_ambiguity_v2
results: []
requirements_ambiguity_v2
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8136
- Accuracy: 0.8189
- F1: 0.8189
- Recall: 0.7604
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall |
|---|---|---|---|---|---|---|
| 0.5247 | 1.0 | 32 | 0.4726 | 0.8110 | 0.8092 | 0.7083 |
| 0.2684 | 2.0 | 64 | 0.5090 | 0.7874 | 0.7897 | 0.7917 |
| 0.1319 | 3.0 | 96 | 0.7653 | 0.8031 | 0.8027 | 0.7292 |
| 0.035 | 4.0 | 128 | 0.8136 | 0.8189 | 0.8189 | 0.7604 |
Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.11.0