---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- anno_ctr
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: annoctr_bert_uncased
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: anno_ctr
      type: anno_ctr
      config: all_tags
      split: test
      args: all_tags
    metrics:
    - name: Precision
      type: precision
      value: 0.7928388746803069
    - name: Recall
      type: recall
      value: 0.7809920945182869
    - name: F1
      type: f1
      value: 0.7868708971553611
    - name: Accuracy
      type: accuracy
      value: 0.936522196415268
---

<!-- 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. -->

# annoctr_bert_uncased

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the anno_ctr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3322
- Precision: 0.7928
- Recall: 0.7810
- F1: 0.7869
- Accuracy: 0.9365

## 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: 1e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.54          | 1.0   | 474  | 0.3452          | 0.6983    | 0.6601 | 0.6786 | 0.9137   |
| 0.3013        | 2.0   | 948  | 0.3466          | 0.7774    | 0.7018 | 0.7376 | 0.9240   |
| 0.0392        | 3.0   | 1422 | 0.3071          | 0.7851    | 0.7517 | 0.7680 | 0.9303   |
| 0.5695        | 4.0   | 1896 | 0.2941          | 0.7810    | 0.7617 | 0.7712 | 0.9334   |
| 0.0021        | 5.0   | 2370 | 0.3109          | 0.7928    | 0.7720 | 0.7823 | 0.9351   |
| 0.0419        | 6.0   | 2844 | 0.3020          | 0.7772    | 0.7796 | 0.7784 | 0.9341   |
| 0.2979        | 7.0   | 3318 | 0.3169          | 0.8019    | 0.7814 | 0.7915 | 0.9374   |
| 0.0017        | 8.0   | 3792 | 0.3260          | 0.7972    | 0.7778 | 0.7874 | 0.9365   |
| 0.0166        | 9.0   | 4266 | 0.3349          | 0.7935    | 0.7789 | 0.7861 | 0.9364   |
| 0.0685        | 10.0  | 4740 | 0.3322          | 0.7928    | 0.7810 | 0.7869 | 0.9365   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1