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
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9354518371400199
- name: Recall
type: recall
value: 0.9511948838774823
- name: F1
type: f1
value: 0.9432576769025368
- name: Accuracy
type: accuracy
value: 0.9868870312591982
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8991033863317628
verified: true
- name: Precision
type: precision
value: 0.9285257381409285
verified: true
- name: Recall
type: recall
value: 0.9145472029711178
verified: true
- name: F1
type: f1
value: 0.9214834614099566
verified: true
- name: loss
type: loss
value: 0.8562382459640503
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:
- Loss: 0.0598
- Precision: 0.9355
- Recall: 0.9512
- F1: 0.9433
- Accuracy: 0.9869
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
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0833 | 1.0 | 1756 | 0.0654 | 0.9202 | 0.9350 | 0.9275 | 0.9833 |
0.034 | 2.0 | 3512 | 0.0610 | 0.9262 | 0.9458 | 0.9359 | 0.9846 |
0.0233 | 3.0 | 5268 | 0.0598 | 0.9355 | 0.9512 | 0.9433 | 0.9869 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1