metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results: []
distilbert-base-uncased-finetuned-ner
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: 0.1242
- Precision: 0.7628
- Recall: 0.7790
- F1: 0.7708
- Accuracy: 0.9658
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0742 | 1.0 | 2531 | 0.1280 | 0.7336 | 0.7800 | 0.7561 | 0.9628 |
| 0.0628 | 2.0 | 5062 | 0.1235 | 0.7665 | 0.7686 | 0.7675 | 0.9656 |
| 0.0581 | 3.0 | 7593 | 0.1242 | 0.7628 | 0.7790 | 0.7708 | 0.9658 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.13.3