metadata
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v1
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 albert/albert-base-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0994
- Precision: 0.9628
- Recall: 0.9740
- F1: 0.9683
- Accuracy: 0.9813
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 |
|---|---|---|---|---|---|---|---|
| 0.0688 | 1.0 | 5285 | 0.1227 | 0.9555 | 0.9670 | 0.9612 | 0.9769 |
| 0.0583 | 2.0 | 10570 | 0.1051 | 0.9581 | 0.9723 | 0.9652 | 0.9803 |
| 0.0798 | 3.0 | 15855 | 0.0994 | 0.9628 | 0.9740 | 0.9683 | 0.9813 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1