metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mobilebert-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: id_nergrit_corpus
type: id_nergrit_corpus
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.6699979179679367
- name: Recall
type: recall
value: 0.6136244458216141
- name: F1
type: f1
value: 0.6405732911990843
- name: Accuracy
type: accuracy
value: 0.8974442203210374
mobilebert-uncased-finetuned-ner
This model is a fine-tuned version of google/mobilebert-uncased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.3800
- Precision: 0.6700
- Recall: 0.6136
- F1: 0.6406
- Accuracy: 0.8974
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 |
|---|---|---|---|---|---|---|---|
| 0.6239 | 1.0 | 1567 | 0.4989 | 0.5842 | 0.4877 | 0.5316 | 0.8688 |
| 0.5356 | 2.0 | 3134 | 0.4003 | 0.6368 | 0.5879 | 0.6113 | 0.8905 |
| 0.4035 | 3.0 | 4701 | 0.3800 | 0.6700 | 0.6136 | 0.6406 | 0.8974 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3