metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: bert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8993712673925718
verified: true
- name: Precision
type: precision
value: 0.9286038802604789
verified: true
- name: Recall
type: recall
value: 0.9149782803329244
verified: true
- name: F1
type: f1
value: 0.9217407280592602
verified: true
- name: loss
type: loss
value: 0.8023650050163269
verified: true
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0593
- eval_precision: 0.9293
- eval_recall: 0.9485
- eval_f1: 0.9388
- eval_accuracy: 0.9858
- eval_runtime: 120.5431
- eval_samples_per_second: 26.97
- eval_steps_per_second: 3.376
- epoch: 2.0
- step: 3512
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
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cpu
- Datasets 2.2.2
- Tokenizers 0.12.1