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: 0.3999
- Precision: 0.7515
- Recall: 0.7934
- F1: 0.7719
- Accuracy: 0.8886
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 |
---|---|---|---|---|---|---|---|
0.7659 | 1.0 | 680 | 0.4549 | 0.6904 | 0.7061 | 0.6982 | 0.8620 |
0.4146 | 2.0 | 1360 | 0.4111 | 0.7310 | 0.7679 | 0.7490 | 0.8788 |
0.2556 | 3.0 | 2040 | 0.3999 | 0.7515 | 0.7934 | 0.7719 | 0.8886 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1