---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-ner-essays-classify_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-classify_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: 0.6855
- Claim: {'precision': 0.4016393442622951, 'recall': 0.3402777777777778, 'f1-score': 0.3684210526315789, 'support': 144.0}
- Majorclaim: {'precision': 0.5882352941176471, 'recall': 0.5555555555555556, 'f1-score': 0.5714285714285715, 'support': 72.0}
- Premise: {'precision': 0.7947494033412887, 'recall': 0.8473282442748091, 'f1-score': 0.8201970443349754, 'support': 393.0}
- Accuracy: 0.6929
- Macro avg: {'precision': 0.5948746805737436, 'recall': 0.5810538592027141, 'f1-score': 0.5866822227983753, 'support': 609.0}
- Weighted avg: {'precision': 0.6773818099562685, 'recall': 0.6929392446633826, 'f1-score': 0.6839621135393265, 'support': 609.0}

## 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: 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                              | Majorclaim                                                                                                       | Premise                                                                                                           | Accuracy | Macro avg                                                                                                         | Weighted avg                                                                                                      |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 267  | 0.6910          | {'precision': 0.40860215053763443, 'recall': 0.2638888888888889, 'f1-score': 0.3206751054852321, 'support': 144.0} | {'precision': 0.625, 'recall': 0.3472222222222222, 'f1-score': 0.44642857142857145, 'support': 72.0}             | {'precision': 0.7521008403361344, 'recall': 0.910941475826972, 'f1-score': 0.8239355581127733, 'support': 393.0}  | 0.6913   | {'precision': 0.5952343302912563, 'recall': 0.5073508623126944, 'f1-score': 0.5303464116755255, 'support': 609.0} | {'precision': 0.6558527749253206, 'recall': 0.6912972085385879, 'f1-score': 0.66030664478005, 'support': 609.0}   |
| 0.7234        | 2.0   | 534  | 0.6855          | {'precision': 0.4016393442622951, 'recall': 0.3402777777777778, 'f1-score': 0.3684210526315789, 'support': 144.0}  | {'precision': 0.5882352941176471, 'recall': 0.5555555555555556, 'f1-score': 0.5714285714285715, 'support': 72.0} | {'precision': 0.7947494033412887, 'recall': 0.8473282442748091, 'f1-score': 0.8201970443349754, 'support': 393.0} | 0.6929   | {'precision': 0.5948746805737436, 'recall': 0.5810538592027141, 'f1-score': 0.5866822227983753, 'support': 609.0} | {'precision': 0.6773818099562685, 'recall': 0.6929392446633826, 'f1-score': 0.6839621135393265, 'support': 609.0} |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3