---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-ner-essays-find_span
  results: []
---

<!-- 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. -->

# bert-ner-essays-find_span

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1978
- B-span: {'precision': 0.8451327433628318, 'recall': 0.8856259659969088, 'f1-score': 0.8649056603773585, 'support': 647.0}
- I-span: {'precision': 0.9613473219215903, 'recall': 0.9557182067703568, 'f1-score': 0.9585244999082401, 'support': 10930.0}
- O: {'precision': 0.89764120320277, 'recall': 0.9040976460331299, 'f1-score': 0.9008578564447822, 'support': 4588.0}
- Accuracy: 0.9383
- Macro avg: {'precision': 0.9013737561623975, 'recall': 0.9151472729334652, 'f1-score': 0.9080960055767937, 'support': 16165.0}
- Weighted avg: {'precision': 0.9386145965884964, 'recall': 0.9382616764614908, 'f1-score': 0.9384103056993428, 'support': 16165.0}

## 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 | B-span                                                                                                            | I-span                                                                                                              | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 196  | 0.1948          | {'precision': 0.8323076923076923, 'recall': 0.8361669242658424, 'f1-score': 0.8342328450269854, 'support': 647.0} | {'precision': 0.9544583371360774, 'recall': 0.9568161024702653, 'f1-score': 0.9556357655229132, 'support': 10930.0} | {'precision': 0.8977621763931549, 'recall': 0.8918918918918919, 'f1-score': 0.89481740651651, 'support': 4588.0}   | 0.9336   | {'precision': 0.8948427352789748, 'recall': 0.894958306209333, 'f1-score': 0.8948953390221361, 'support': 16165.0}  | {'precision': 0.9334776100904544, 'recall': 0.9335601608413239, 'f1-score': 0.9335149909678719, 'support': 16165.0} |
| No log        | 2.0   | 392  | 0.1840          | {'precision': 0.8016528925619835, 'recall': 0.8995363214837713, 'f1-score': 0.8477785870356882, 'support': 647.0} | {'precision': 0.9520368530394725, 'recall': 0.9643183897529735, 'f1-score': 0.9581382664424344, 'support': 10930.0} | {'precision': 0.9198717948717948, 'recall': 0.8757628596338274, 'f1-score': 0.8972755694506476, 'support': 4588.0} | 0.9366   | {'precision': 0.8911871801577503, 'recall': 0.9132058569568574, 'f1-score': 0.9010641409762568, 'support': 16165.0} | {'precision': 0.936888587694453, 'recall': 0.9365914011753789, 'f1-score': 0.936446910650632, 'support': 16165.0}   |
| 0.2568        | 3.0   | 588  | 0.1978          | {'precision': 0.8451327433628318, 'recall': 0.8856259659969088, 'f1-score': 0.8649056603773585, 'support': 647.0} | {'precision': 0.9613473219215903, 'recall': 0.9557182067703568, 'f1-score': 0.9585244999082401, 'support': 10930.0} | {'precision': 0.89764120320277, 'recall': 0.9040976460331299, 'f1-score': 0.9008578564447822, 'support': 4588.0}   | 0.9383   | {'precision': 0.9013737561623975, 'recall': 0.9151472729334652, 'f1-score': 0.9080960055767937, 'support': 16165.0} | {'precision': 0.9386145965884964, 'recall': 0.9382616764614908, 'f1-score': 0.9384103056993428, 'support': 16165.0} |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1