---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tner/ontonotes5
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: 'Hi! I am jack. I live in California and I work for Apple '
  example_title: Example 1
- text: 'Thi book is amazing! I bought it on Amazon for 4$. '
  example_title: Example 2
base_model: bert-base-cased
model-index:
- name: bert-finetuned-ner-ontonotes
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: ontonotes5
      type: ontonotes5
      config: ontonotes5
      split: train
      args: ontonotes5
    metrics:
    - type: precision
      value: 0.8567258883248731
      name: Precision
    - type: recall
      value: 0.8841595180407308
      name: Recall
    - type: f1
      value: 0.8702265476459025
      name: F1
    - type: accuracy
      value: 0.9754933764288157
      name: Accuracy
---

<!-- 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-finetuned-ner-ontonotes

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1503
- Precision: 0.8567
- Recall: 0.8842
- F1: 0.8702
- Accuracy: 0.9755

## Model description

Token classification experiment, NER, on business topics.

## Intended uses & limitations

The model can be used on token classification, in particular NER. It is fine tuned on business topic.

## Training and evaluation data

The dataset used is [ontonotes5](https://huggingface.co/datasets/tner/ontonotes5)

## 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: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0842        | 1.0   | 7491  | 0.0950          | 0.8524    | 0.8715 | 0.8618 | 0.9745   |
| 0.0523        | 2.0   | 14982 | 0.1044          | 0.8449    | 0.8827 | 0.8634 | 0.9744   |
| 0.036         | 3.0   | 22473 | 0.1118          | 0.8529    | 0.8843 | 0.8683 | 0.9760   |
| 0.0231        | 4.0   | 29964 | 0.1240          | 0.8589    | 0.8805 | 0.8696 | 0.9752   |
| 0.0118        | 5.0   | 37455 | 0.1416          | 0.8570    | 0.8804 | 0.8685 | 0.9753   |
| 0.0077        | 6.0   | 44946 | 0.1503          | 0.8567    | 0.8842 | 0.8702 | 0.9755   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1