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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- generated_from_keras_callback |
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- named entity recognition |
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- bert-base finetuned |
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- umair akram |
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datasets: |
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- conll2003 |
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metrics: |
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- seqeval |
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pipeline_tag: token-classification |
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base_model: bert-base-cased |
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model-index: |
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- name: MUmairAB/bert-ner |
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results: [] |
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--- |
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# MUmairAB/bert-ner |
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The model training notebook is available on my [GitHub Repo](https://github.com/MUmairAB/BERT-based-NER-using-HuggingFace-Transformers/tree/main). |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on [Cnoll2003](https://huggingface.co/datasets/conll2003) dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0003 |
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- Validation Loss: 0.0880 |
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- Epoch: 19 |
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## How to use this model |
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``` |
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#Install the transformers library |
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!pip install transformers |
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#Import the pipeline |
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from transformers import pipeline |
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#Import the model from HuggingFace |
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checkpoint = "MUmairAB/bert-ner" |
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model = pipeline(task="token-classification", |
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model=checkpoint) |
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#Use the model |
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raw_text = "My name is umair and i work at Swits AI in Antarctica." |
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model(raw_text) |
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``` |
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## Model description |
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Model: "tf_bert_for_token_classification" |
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``` |
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_________________________________________________________________ |
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Layer (type) Output Shape Param # |
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================================================================= |
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bert (TFBertMainLayer) multiple 107719680 |
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dropout_37 (Dropout) multiple 0 |
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classifier (Dense) multiple 6921 |
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================================================================= |
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Total params: 107,726,601 |
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Trainable params: 107,726,601 |
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Non-trainable params: 0 |
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_________________________________________________________________ |
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``` |
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## Intended uses & limitations |
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This model can be used for named entity recognition tasks. It is trained on [Conll2003](https://huggingface.co/datasets/conll2003) dataset. The model can classify four types of named entities: |
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1. persons, |
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2. locations, |
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3. organizations, and |
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4. names of miscellaneous entities that do not belong to the previous three groups. |
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## Training and evaluation data |
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The model is evaluated on [seqeval](https://github.com/chakki-works/seqeval) metric and the result is as follows: |
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``` |
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{'LOC': {'precision': 0.9655361050328227, |
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'recall': 0.9608056614044638, |
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'f1': 0.9631650750341064, |
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'number': 1837}, |
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'MISC': {'precision': 0.8789144050104384, |
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'recall': 0.913232104121475, |
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'f1': 0.8957446808510638, |
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'number': 922}, |
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'ORG': {'precision': 0.9075144508670521, |
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'recall': 0.9366144668158091, |
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'f1': 0.9218348623853211, |
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'number': 1341}, |
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'PER': {'precision': 0.962011771000535, |
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'recall': 0.9761129207383279, |
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'f1': 0.9690110482349771, |
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'number': 1842}, |
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'overall_precision': 0.9374068554396423, |
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'overall_recall': 0.9527095254123191, |
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'overall_f1': 0.944996244053084, |
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'overall_accuracy': 0.9864013657502796} |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17560, '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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 0.1775 | 0.0635 | 0 | |
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| 0.0470 | 0.0559 | 1 | |
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| 0.0278 | 0.0603 | 2 | |
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| 0.0174 | 0.0603 | 3 | |
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| 0.0124 | 0.0615 | 4 | |
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| 0.0077 | 0.0722 | 5 | |
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| 0.0060 | 0.0731 | 6 | |
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| 0.0038 | 0.0757 | 7 | |
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| 0.0043 | 0.0731 | 8 | |
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| 0.0041 | 0.0735 | 9 | |
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| 0.0019 | 0.0724 | 10 | |
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| 0.0019 | 0.0786 | 11 | |
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| 0.0010 | 0.0843 | 12 | |
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| 0.0008 | 0.0814 | 13 | |
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| 0.0011 | 0.0867 | 14 | |
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| 0.0008 | 0.0883 | 15 | |
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| 0.0005 | 0.0861 | 16 | |
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| 0.0005 | 0.0869 | 17 | |
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| 0.0003 | 0.0880 | 18 | |
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| 0.0003 | 0.0880 | 19 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- TensorFlow 2.12.0 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
