---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
datasets:
- conll2003job
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_xlm-roberta-large-finetuned-conlljob01
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003job
      type: conll2003job
      config: conll2003job
      split: test
      args: conll2003job
    metrics:
    - name: Precision
      type: precision
      value: 0.9057427125152732
    - name: Recall
      type: recall
      value: 0.9187322946175638
    - name: F1
      type: f1
      value: 0.9121912630746243
    - name: Accuracy
      type: accuracy
      value: 0.9825347259610208
---

<!-- 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. -->

# my_xlm-roberta-large-finetuned-conlljob01

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003job dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1690
- Precision: 0.9057
- Recall: 0.9187
- F1: 0.9122
- Accuracy: 0.9825

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0372        | 1.0   | 896  | 0.1439          | 0.8943    | 0.9184 | 0.9062 | 0.9816   |
| 0.0043        | 2.0   | 1792 | 0.1532          | 0.9047    | 0.9209 | 0.9127 | 0.9824   |
| 0.0019        | 3.0   | 2688 | 0.1652          | 0.9102    | 0.9186 | 0.9143 | 0.9828   |
| 0.0013        | 4.0   | 3584 | 0.1690          | 0.9057    | 0.9187 | 0.9122 | 0.9825   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1