metadata
library_name: transformers
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: 0.1008
- Precision: 0.7986
- Recall: 0.7968
- F1: 0.7977
- Accuracy: 0.9750
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: 512
- eval_batch_size: 512
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 2048
- 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 | 0.8696 | 5 | 0.0968 | 0.8048 | 0.7943 | 0.7995 | 0.9757 |
| No log | 1.9130 | 11 | 0.0984 | 0.8030 | 0.7966 | 0.7998 | 0.9754 |
| No log | 2.9565 | 17 | 0.1003 | 0.8008 | 0.7965 | 0.7987 | 0.9751 |
| No log | 4.0 | 23 | 0.1008 | 0.7986 | 0.7968 | 0.7977 | 0.9750 |
| No log | 4.3478 | 25 | 0.1008 | 0.7986 | 0.7968 | 0.7977 | 0.9750 |
Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1