metadata
license: gpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: IceBERT-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
- name: Precision
type: precision
value: 0.8948412698412699
- name: Recall
type: recall
value: 0.86222965706775
- name: F1
type: f1
value: 0.878232824195217
- name: Accuracy
type: accuracy
value: 0.9851596438314519
IceBERT-finetuned-ner
This model is a fine-tuned version of vesteinn/IceBERT on the mim_gold_ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0787
- Precision: 0.8948
- Recall: 0.8622
- F1: 0.8782
- Accuracy: 0.9852
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0526 | 1.0 | 2904 | 0.0746 | 0.8802 | 0.8539 | 0.8668 | 0.9836 |
| 0.0264 | 2.0 | 5808 | 0.0711 | 0.8777 | 0.8594 | 0.8684 | 0.9843 |
| 0.0161 | 3.0 | 8712 | 0.0787 | 0.8948 | 0.8622 | 0.8782 | 0.9852 |
Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3