claims-data-model
This model is a fine-tuned version of distilbert-base-uncased on the ontonotes5 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0943
- Precision: 0.8523
- Recall: 0.8809
- F1: 0.8664
- Accuracy: 0.9742
Model description
lebel2id dict:
label2id = {
"O": 0,
"B-CARDINAL": 1,
"B-DATE": 2,
"I-DATE": 3,
"B-PERSON": 4,
"I-PERSON": 5,
"B-NORP": 6,
"B-GPE": 7,
"I-GPE": 8,
"B-LAW": 9,
"I-LAW": 10,
"B-ORG": 11,
"I-ORG": 12,
"B-PERCENT": 13,
"I-PERCENT": 14,
"B-ORDINAL": 15,
"B-MONEY": 16,
"I-MONEY": 17,
"B-WORK_OF_ART": 18,
"I-WORK_OF_ART": 19,
"B-FAC": 20,
"B-TIME": 21,
"I-CARDINAL": 22,
"B-LOC": 23,
"B-QUANTITY": 24,
"I-QUANTITY": 25,
"I-NORP": 26,
"I-LOC": 27,
"B-PRODUCT": 28,
"I-TIME": 29,
"B-EVENT": 30,
"I-EVENT": 31,
"I-FAC": 32,
"B-LANGUAGE": 33,
"I-PRODUCT": 34,
"I-ORDINAL": 35,
"I-LANGUAGE": 36
}
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0736 | 1.0 | 843 | 0.0943 | 0.8523 | 0.8809 | 0.8664 | 0.9742 |
| 0.0711 | 2.0 | 1686 | 0.0943 | 0.8523 | 0.8809 | 0.8664 | 0.9742 |
| 0.0714 | 3.0 | 2529 | 0.0943 | 0.8523 | 0.8809 | 0.8664 | 0.9742 |
Framework versions
- Transformers 4.18.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.12.1