metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9280017722640673
- name: Recall
type: recall
value: 0.9372413021590782
- name: F1
type: f1
value: 0.9325986530862136
- name: Accuracy
type: accuracy
value: 0.9837164598789457
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0603
- Precision: 0.9280
- Recall: 0.9372
- F1: 0.9326
- Accuracy: 0.9837
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 |
|---|---|---|---|---|---|---|---|
| 0.2436 | 1.0 | 878 | 0.0687 | 0.9063 | 0.9195 | 0.9128 | 0.9800 |
| 0.0501 | 2.0 | 1756 | 0.0587 | 0.9240 | 0.9356 | 0.9297 | 0.9835 |
| 0.0303 | 3.0 | 2634 | 0.0603 | 0.9280 | 0.9372 | 0.9326 | 0.9837 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0