metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0025
- Precision: 0.6402
- Recall: 0.7307
- F1: 0.6824
- Accuracy: 0.9992
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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 383 | 0.0032 | 0.6972 | 0.528 | 0.6009 | 0.9991 |
| 0.0292 | 2.0 | 766 | 0.0023 | 0.7590 | 0.672 | 0.7129 | 0.9994 |
| 0.0018 | 3.0 | 1149 | 0.0023 | 0.7660 | 0.7333 | 0.7493 | 0.9994 |
| 0.0009 | 4.0 | 1532 | 0.0023 | 0.7520 | 0.736 | 0.7439 | 0.9994 |
| 0.0009 | 5.0 | 1915 | 0.0025 | 0.6402 | 0.7307 | 0.6824 | 0.9992 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2