---
license: mit
base_model: dslim/bert-large-NER
tags:
- generated_from_trainer
datasets:
- job-titles
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: job-titles
      type: job-titles
      config: job-titles
      split: test
      args: job-titles
    metrics:
    - name: Precision
      type: precision
      value: 0.9992003198720512
    - name: Recall
      type: recall
      value: 0.9996
    - name: F1
      type: f1
      value: 0.9994001199760049
    - name: Accuracy
      type: accuracy
      value: 0.6346958244661334
---

<!-- 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_awesome_wnut_model

This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on the job-titles dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6603
- Precision: 0.9992
- Recall: 0.9996
- F1: 0.9994
- Accuracy: 0.6347

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6666        | 1.0   | 4587 | 0.6615          | 1.0       | 1.0    | 1.0    | 0.6331   |
| 0.6617        | 2.0   | 9174 | 0.6603          | 0.9992    | 0.9996 | 0.9994 | 0.6347   |


### Framework versions

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