metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT-finetuned-ner-S800
results: []
DistilBERT-finetuned-ner-S800
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: 0.0834
- Precision: 0.5329
- Recall: 0.6129
- F1: 0.5701
- Accuracy: 0.9689
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 55 | 0.1363 | 0.2460 | 0.2987 | 0.2698 | 0.9436 |
| No log | 2.0 | 110 | 0.0926 | 0.3943 | 0.4656 | 0.4270 | 0.9636 |
| No log | 3.0 | 165 | 0.0823 | 0.4937 | 0.6059 | 0.5441 | 0.9683 |
| No log | 4.0 | 220 | 0.0802 | 0.5187 | 0.5849 | 0.5498 | 0.9696 |
| No log | 5.0 | 275 | 0.0834 | 0.5329 | 0.6129 | 0.5701 | 0.9689 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3