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: 0.8794544654641443
- name: Recall
type: recall
value: 0.8945072155722117
- name: F1
type: f1
value: 0.8869169763185625
- name: Accuracy
type: accuracy
value: 0.9731996759178356
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: 0.1902
- Precision: 0.8795
- Recall: 0.8945
- F1: 0.8869
- Accuracy: 0.9732
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 439 | 0.1942 | 0.8818 | 0.8931 | 0.8874 | 0.9732 |
| 0.0009 | 2.0 | 878 | 0.1902 | 0.8817 | 0.8933 | 0.8875 | 0.9729 |
| 0.001 | 3.0 | 1317 | 0.1894 | 0.8794 | 0.8952 | 0.8872 | 0.9733 |
| 0.0009 | 4.0 | 1756 | 0.1902 | 0.8795 | 0.8945 | 0.8869 | 0.9732 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2