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
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9278601460500111
- name: Recall
type: recall
value: 0.9381362568519969
- name: F1
type: f1
value: 0.9329699059909885
- name: Accuracy
type: accuracy
value: 0.9841295057746994
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: 0.0602
- Precision: 0.9279
- Recall: 0.9381
- F1: 0.9330
- Accuracy: 0.9841
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 |
---|---|---|---|---|---|---|---|
0.2379 | 1.0 | 878 | 0.0679 | 0.9193 | 0.9242 | 0.9217 | 0.9818 |
0.0508 | 2.0 | 1756 | 0.0609 | 0.9220 | 0.9355 | 0.9287 | 0.9835 |
0.0306 | 3.0 | 2634 | 0.0602 | 0.9279 | 0.9381 | 0.9330 | 0.9841 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3