metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT-finetuned-ner-copious
results: []
DistilBERT-finetuned-ner-copious
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0755
- Precision: 0.6056
- Recall: 0.6565
- F1: 0.6300
- Accuracy: 0.9752
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.1322 | 0.3129 | 0.2884 | 0.3002 | 0.9529 |
| No log | 2.0 | 126 | 0.0842 | 0.5190 | 0.5739 | 0.5451 | 0.9711 |
| No log | 3.0 | 189 | 0.0772 | 0.5765 | 0.6174 | 0.5962 | 0.9740 |
| No log | 4.0 | 252 | 0.0751 | 0.6035 | 0.6464 | 0.6242 | 0.9751 |
| No log | 5.0 | 315 | 0.0755 | 0.6056 | 0.6565 | 0.6300 | 0.9752 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3