---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ontonotes5
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: claims-data-model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ontonotes5
      type: ontonotes5
      config: ontonotes5
      split: train
      args: ontonotes5
    metrics:
    - name: Precision
      type: precision
      value: 0.87946959304984
    - name: Recall
      type: recall
      value: 0.8952708992738783
    - name: F1
      type: f1
      value: 0.8872999031231259
    - name: Accuracy
      type: accuracy
      value: 0.9781072801388117
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# claims-data-model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0846
- Precision: 0.8795
- Recall: 0.8953
- F1: 0.8873
- Accuracy: 0.9781

## Label IDS
```
{
    "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
}
```

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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1118        | 1.0   | 3371  | 0.0934          | 0.8645    | 0.8723 | 0.8684 | 0.9744   |
| 0.0727        | 2.0   | 6742  | 0.0833          | 0.8761    | 0.8910 | 0.8835 | 0.9771   |
| 0.0513        | 3.0   | 10113 | 0.0846          | 0.8795    | 0.8953 | 0.8873 | 0.9781   |


### Framework versions

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