metadata
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test_NER
results: []
test_NER
This model is a fine-tuned version of distilbert/distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2793
- Precision: 0.4818
- Recall: 0.4545
- F1: 0.4678
- Accuracy: 0.9147
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 56 | 0.3638 | 0.1818 | 0.0034 | 0.0067 | 0.8765 |
| No log | 2.0 | 112 | 0.2939 | 0.4912 | 0.4322 | 0.4599 | 0.9120 |
| No log | 3.0 | 168 | 0.2793 | 0.4818 | 0.4545 | 0.4678 | 0.9147 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2