metadata
base_model: bert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: NER_training_base_uncased_with_randomization
results: []
NER_training_base_uncased_with_randomization
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0472
- Precision: 0.9550
- Recall: 0.9576
- F1: 0.9563
- Accuracy: 0.9849
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: 32
- seed: 12
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0508 | 1.0 | 9836 | 0.0472 | 0.9550 | 0.9576 | 0.9563 | 0.9849 |
| 0.035 | 2.0 | 19672 | 0.0473 | 0.9590 | 0.9644 | 0.9617 | 0.9870 |
| 0.021 | 3.0 | 29508 | 0.0537 | 0.9592 | 0.9636 | 0.9614 | 0.9870 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0