readme: ---
  license: apache-2.0
  tags:
  - generated_from_keras_callback
  model-index:
  - name: bert-finetuned-ner-per-v2
    results: []
  datasets:
  - wikiann
  - BeardedJohn/NERPERDemo
  - BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2
  language:
  - en
  library_name: transformers
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # bert-finetuned-ner-per-v2
  
  This model is a fine-tuned version of [BERT](https://huggingface.co/bert-base-cased) on three datasets:
  
  - [conll-endava mixed dataset, second version](https://huggingface.co/datasets/BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2)
  - [NERPERDemo dataset](https://huggingface.co/datasets/BeardedJohn/NERPERDemo)
  - 12000 instances of the [wikiann, english version dataset](https://huggingface.co/datasets/wikiann/viewer/en/train).
  
  It achieves the following results on the conll-endava mixed, second version evaluation set:
  - Train Loss: 0.0190
  - Validation Loss: 0.0310
  - Epoch: 2
  
  It achieves the following results on the NERPERDemo evaluation set:
  - Train Loss: 0.0005
  - Validation Loss: 0.0002
  - Epoch: 2
  
  It achieves the following results on the wikiann evaluation set:
  - Train Loss: 0.1217
  - Validation Loss: 0.3073
  - Epoch: 2
  
  ## Model description
  
  The model is a fine-tuned version of BERT with the intent of solving the NER task. It is trained to recognize four classes of entities:
  
  - Person (PER)
  - Organisation (ORG)
  - Location (LOC)
  - Miscellaneous (MISC)*
  
  * The MISC label maps data corresponding to the [conll-endava](https://huggingface.co/datasets/BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2) dataset.
  
  ## Intended uses & limitations
  
  It can be used as a general purpose model for recognizing the 4 mentioned entities, but it may have some phrase specific bias introduced by the two datasets (conll-endava and NERPERDemo). 
  The model is part of a project and is fine-tuned to meet the specific requirements, but feel free to test it in your own environment as it has fine-tuned on general data too.
  
  ## Training and evaluation data
  
  Training and evaluation data are from the three mentioned datasets.
  
  ## Training procedure
  
  Training is inspired from [HuggingFace tutorial](https://huggingface.co/course/chapter7/2?fw=tf).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1875, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
  - training_precision: mixed_float16
  
  ### Training results
  
  On conll-endava mixed, second version:
  
  | Train Loss | Validation Loss | Epoch |
  |:----------:|:---------------:|:-----:|
  | 0.2091     | 0.0391          | 0     |
  | 0.0336     | 0.0322          | 1     |
  | 0.0190     | 0.0310          | 2     |
  
  
  On NERPERDemo:
  
  | Train Loss | Validation Loss | Epoch |
  |:----------:|:---------------:|:-----:|
  | 0.0202     | 0.0005          | 0     |
  | 0.0009     | 0.0002          | 1     |
  | 0.0005     | 0.0002          | 2     |
  
  On wikiann:
  
  | Train Loss | Validation Loss | Epoch |
  |:----------:|:---------------:|:-----:|
  | 0.2975     | 0.2869          | 0     |
  | 0.1755     | 0.2934          | 1     |
  | 0.1217     | 0.3073          | 2     |
  
  ### Framework versions
  
  - Transformers 4.25.1
  - TensorFlow 2.9.2
  - Datasets 2.8.0
  - Tokenizers 0.13.2