metadata
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoint-10000-finetuned-ner
results: []
checkpoint-10000-finetuned-ner
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1752
- Precision: 0.7371
- Recall: 0.7711
- F1: 0.7537
- Accuracy: 0.9457
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: 16
- seed: 42
- distributed_type: tpu
- 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 |
|---|---|---|---|---|---|---|---|
| 0.4149 | 1.0 | 878 | 0.2236 | 0.6673 | 0.6842 | 0.6757 | 0.9290 |
| 0.1795 | 2.0 | 1756 | 0.1849 | 0.7084 | 0.7581 | 0.7325 | 0.9410 |
| 0.122 | 3.0 | 2634 | 0.1752 | 0.7371 | 0.7711 | 0.7537 | 0.9457 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1