metadata
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-distilbert
results: []
ner-distilbert
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0003
- Precision: 0.9988
- Recall: 0.9980
- F1: 0.9984
- Accuracy: 0.9998
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0002 | 0.16 | 250 | 0.0011 | 0.9961 | 0.9980 | 0.9971 | 0.9996 |
| 0.0001 | 0.31 | 500 | 0.0008 | 0.9977 | 0.9977 | 0.9977 | 0.9997 |
| 0.0004 | 0.47 | 750 | 0.0005 | 0.9992 | 0.9977 | 0.9984 | 0.9998 |
| 0.0002 | 0.63 | 1000 | 0.0005 | 0.9984 | 0.9977 | 0.9980 | 0.9997 |
| 0.0002 | 0.79 | 1250 | 0.0003 | 0.9988 | 0.9980 | 0.9984 | 0.9998 |
| 0.0 | 0.94 | 1500 | 0.0003 | 0.9988 | 0.9980 | 0.9984 | 0.9998 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1