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 an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6121
- Precision: 0.5944
- Recall: 0.5283
- F1: 0.5594
- Accuracy: 0.7981
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3792 | 1.0 | 2015 | 0.4403 | 0.5739 | 0.5797 | 0.5768 | 0.7937 |
| 0.2557 | 2.0 | 4030 | 0.5227 | 0.6205 | 0.5021 | 0.5551 | 0.8048 |
| 0.1653 | 3.0 | 6045 | 0.6121 | 0.5944 | 0.5283 | 0.5594 | 0.7981 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1