metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-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: 0.9244444444444444
- name: Recall
type: recall
value: 0.9451363177381353
- name: F1
type: f1
value: 0.9346758758425564
- name: Accuracy
type: accuracy
value: 0.9856066403720493
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:
- Loss: 0.0562
- Precision: 0.9244
- Recall: 0.9451
- F1: 0.9347
- Accuracy: 0.9856
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 439 | 0.0736 | 0.8930 | 0.9211 | 0.9068 | 0.9796 |
| 0.1905 | 2.0 | 878 | 0.0588 | 0.9165 | 0.9408 | 0.9285 | 0.9848 |
| 0.0488 | 3.0 | 1317 | 0.0562 | 0.9244 | 0.9451 | 0.9347 | 0.9856 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0