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-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: 0.9297953751455664
- name: Recall
type: recall
value: 0.9405923931336251
- name: F1
type: f1
value: 0.9351627206559023
- name: Accuracy
type: accuracy
value: 0.9852733251783247
distilbert-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: 0.0599
- Precision: 0.9298
- Recall: 0.9406
- F1: 0.9352
- Accuracy: 0.9853
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 |
|---|---|---|---|---|---|---|---|
| 0.0785 | 1.0 | 1756 | 0.0649 | 0.9037 | 0.9298 | 0.9166 | 0.9823 |
| 0.0403 | 2.0 | 3512 | 0.0572 | 0.9161 | 0.9334 | 0.9246 | 0.9840 |
| 0.024 | 3.0 | 5268 | 0.0599 | 0.9298 | 0.9406 | 0.9352 | 0.9853 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3