metadata
license: apache-2.0
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: 0.9139204232337705
- name: Recall
type: recall
value: 0.9276205392102025
- name: F1
type: f1
value: 0.9207195203197868
- name: Accuracy
type: accuracy
value: 0.9817306623032075
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: 0.0662
- Precision: 0.9139
- Recall: 0.9276
- F1: 0.9207
- Accuracy: 0.9817
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: 48
- eval_batch_size: 48
- 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 | 293 | 0.0861 | 0.8786 | 0.9014 | 0.8899 | 0.9765 |
| 0.1963 | 2.0 | 586 | 0.0682 | 0.9031 | 0.9218 | 0.9124 | 0.9805 |
| 0.1963 | 3.0 | 879 | 0.0662 | 0.9139 | 0.9276 | 0.9207 | 0.9817 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.0
- Datasets 2.10.1
- Tokenizers 0.11.0