metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v2_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v2_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v2_wikigold_split
type: article250v2_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.4664981036662453
- name: Recall
type: recall
value: 0.5280480824270177
- name: F1
type: f1
value: 0.49536850583971004
- name: Accuracy
type: accuracy
value: 0.9042507513954486
Article_250v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of bert-base-cased on the article250v2_wikigold_split dataset. It achieves the following results on the evaluation set:
- Loss: 0.2900
- Precision: 0.4665
- Recall: 0.5280
- F1: 0.4954
- Accuracy: 0.9043
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 | 29 | 0.4904 | 0.1788 | 0.0487 | 0.0765 | 0.8034 |
| No log | 2.0 | 58 | 0.3224 | 0.4091 | 0.4825 | 0.4428 | 0.8951 |
| No log | 3.0 | 87 | 0.2900 | 0.4665 | 0.5280 | 0.4954 | 0.9043 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6