---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
  results: []
language:
- en
metrics:
- seqeval
pipeline_tag: token-classification
---

# bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased).

It achieves the following results on the evaluation set:
- Loss: 0.1312
- Person
  - Precision: 0.8860048426150121
  - Recall: 0.9401849948612538
  - F1: 0.912291199202194
  - Number': 29190
- Location
  - Precision: 0.8686381704207632
  - Recall: 0.8152889539136796
  - F1: 0.841118472477534
  - Number: 95690
- Organization
  - Precision: 0.7919078915181266
  - Recall': 0.7449641777764141
  - F1': 0.7677190874452579
  - Number': 65183
- Product
  - Precision: 0.7065968977761166
  - Recall: 0.8295304958315051
  - F1: 0.7631446160056513
  - Number: 9116
- Art
  - Precision: 0.8407258064516129
  - Recall: 0.8614333386302241
  - F1: 0.8509536143159878
  - Number: 6293
- Other
  - Precision: 0.7303024586555996
  - Recall: 0.8314124132006586
  - F1: 0.7775843599357258
  - Nnumber: 13969
- Building
  - Precision: 0.5162234691388143
  - Recall: 0.3648904983617865
  - F1: 0.4275611234592847
  - Number: 5799
- Event
  - Precision: 0.605920892987139
  - Recall: 0.35144264602392683
  - F1: 0.44486014608943525
  - Number': 7105
- Overall
  - Precision: 0.8203
  - Recall: 0.7886
  - F1: 0.8041
  - Accuracy: 0.9498

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/tree/main/Token%20Classification/Monolingual/DFKI%20SLT%20few%20NERd

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co/datasets/DFKI-SLT/few-nerd

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 | Person Precision | Person Recall | Person F1 | Person Number | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Product Precision | Product Recall | Product F1 | Product Number | Art Precision | Art Recall | Art F1 | Art Number | Other Precision | Other Recall | Other F1 | Other Number | Building Precision | Building Recall | Building F1 | Building Number | Event Precision | Event Recall | Event F1 | Event Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| 0.1796 | 1.0 | 11293 | 0.1427 | 0.8741 | 0.9272 | 0.8999 | 29190 | 0.8576 | 0.8072 | 0.8316 | 95690 | 0.7699 | 0.7688 | 0.7694 | 65183 | 0.6711 | 0.75 | 0.7084 | 9116 | 0.8347 | 0.8154 | 0.8249 | 6293 | 0.6743 | 0.8195 | 0.7398 | 13969 | 0.4812 | 0.3951 | 0.4339 | 5799 | 0.5998 | 0.3253 | 0.4218 | 7105 | 0.8000 | 0.7852 | 0.7925 | 0.9483 |
| 0.1542 | 2.0 | 22586 | 0.1312 | 0.8860 | 0.9402 | 0.9123 | 29190 | 0.8686 | 0.8153 | 0.8411 | 95690 | 0.7919 | 0.7450 | 0.7677 | 65183 | 0.7066 | 0.8295 | 0.7631 | 9116 | 0.8407 | 0.8614 | 0.8510 | 6293 | 0.7303 | 0.8314 | 0.7776 | 13969 | 0.5162 | 0.3649 | 0.4276 | 5799 | 0.6059 | 0.3514 | 0.4449 | 7105 | 0.8203 | 0.7886 | 0.8041 | 0.9498 |

### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3