metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
config: ncbi_disease
split: validation
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.8144458281444583
- name: Recall
type: recall
value: 0.8605263157894737
- name: F1
type: f1
value: 0.836852207293666
- name: Accuracy
type: accuracy
value: 0.9804873249598268
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the ncbi_disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.0735
- Precision: 0.8144
- Recall: 0.8605
- F1: 0.8369
- Accuracy: 0.9805
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
- 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 | 340 | 0.0761 | 0.7560 | 0.8316 | 0.7920 | 0.9758 |
| 0.1236 | 2.0 | 680 | 0.0719 | 0.8105 | 0.8355 | 0.8228 | 0.9794 |
| 0.0397 | 3.0 | 1020 | 0.0735 | 0.8144 | 0.8605 | 0.8369 | 0.9805 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2