metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- hausa_voa_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-hausa_ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: hausa_voa_ner
type: hausa_voa_ner
config: hausa_voa_ner
split: validation
args: hausa_voa_ner
metrics:
- name: Precision
type: precision
value: 0.6781609195402298
- name: Recall
type: recall
value: 0.7763157894736842
- name: F1
type: f1
value: 0.7239263803680982
- name: Accuracy
type: accuracy
value: 0.9516353514265832
bert-finetuned-hausa_ner
This model is a fine-tuned version of bert-base-cased on the hausa_voa_ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.1734
- Precision: 0.6782
- Recall: 0.7763
- F1: 0.7239
- Accuracy: 0.9516
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 127 | 0.2162 | 0.6992 | 0.7342 | 0.7163 | 0.9516 |
| No log | 2.0 | 254 | 0.1702 | 0.6900 | 0.7789 | 0.7318 | 0.9518 |
| No log | 3.0 | 381 | 0.1734 | 0.6782 | 0.7763 | 0.7239 | 0.9516 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3