metadata
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT-full-finetuned-ner-pablo
results: []
BERT-full-finetuned-ner-pablo
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1071
- Precision: 0.7993
- Recall: 0.7887
- F1: 0.7940
- Accuracy: 0.9768
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 0.9923 | 97 | 0.1037 | 0.7655 | 0.7399 | 0.7525 | 0.9725 |
| No log | 1.9949 | 195 | 0.0907 | 0.8123 | 0.7488 | 0.7792 | 0.9759 |
| No log | 2.9974 | 293 | 0.0922 | 0.7739 | 0.7872 | 0.7805 | 0.9758 |
| No log | 4.0 | 391 | 0.0986 | 0.7856 | 0.7895 | 0.7875 | 0.9760 |
| No log | 4.9616 | 485 | 0.1071 | 0.7993 | 0.7887 | 0.7940 | 0.9768 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1