metadata
base_model: distilbert-base-uncased
library_name: peft
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-ner-lorafinetune-runs-v1
results: []
distilbert-ner-lorafinetune-runs-v1
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0735
- Precision: 0.9638
- Recall: 0.9778
- F1: 0.9708
- Accuracy: 0.9888
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: 0.0004
- 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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0808 | 1.0 | 2643 | 0.1186 | 0.9399 | 0.9629 | 0.9513 | 0.9818 |
| 0.0648 | 2.0 | 5286 | 0.0807 | 0.9556 | 0.9736 | 0.9645 | 0.9868 |
| 0.0366 | 3.0 | 7929 | 0.0761 | 0.9611 | 0.9770 | 0.9690 | 0.9883 |
| 0.0306 | 4.0 | 10572 | 0.0735 | 0.9638 | 0.9778 | 0.9708 | 0.9888 |
Framework versions
- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.4.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1