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: 0.0852
• Precision: 0.8690
• Recall: 0.8939
• F1: 0.8813
• Accuracy: 0.9767

Label IDS

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
}

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1112        | 1.0   | 3371  | 0.0924          | 0.8506    | 0.8752 | 0.8627 | 0.9741   |
| 0.0752        | 2.0   | 6742  | 0.0870          | 0.8543    | 0.8943 | 0.8738 | 0.9754   |
| 0.0538        | 3.0   | 10113 | 0.0852          | 0.8690    | 0.8939 | 0.8813 | 0.9767   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3