MUmairAB/bert-ner

The model training notebook is available on my GitHub Repo.

This model is a fine-tuned version of bert-base-cased on Cnoll2003 dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0003
• Validation Loss: 0.0880
• Epoch: 19

How to use this model

#Install the transformers library
!pip install transformers

#Import the pipeline
from transformers import pipeline

#Import the model from HuggingFace
checkpoint = "MUmairAB/bert-ner"
model = pipeline(task="token-classification",
                 model=checkpoint)

#Use the model
raw_text = "My name is umair and i work at Swits AI in Antarctica."
model(raw_text)

Model description

Model: "tf_bert_for_token_classification" ``` _________________________________________________________________ Layer (type) Output Shape Param #

bert (TFBertMainLayer) multiple 107719680
dropout_37 (Dropout) multiple 0

classifier (Dense) multiple 6921

Total params: 107,726,601 Trainable params: 107,726,601 Non-trainable params: 0

---

## Intended uses & limitations

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:
1. persons,
2. locations,
3. organizations, and
4. names of miscellaneous entities that do not belong to the previous three groups.

## Training and evaluation data

The model is evaluated on [seqeval](https://github.com/chakki-works/seqeval) metric and the result is as follows:

{'LOC': {'precision': 0.9655361050328227, 'recall': 0.9608056614044638, 'f1': 0.9631650750341064, 'number': 1837}, 'MISC': {'precision': 0.8789144050104384, 'recall': 0.913232104121475, 'f1': 0.8957446808510638, 'number': 922}, 'ORG': {'precision': 0.9075144508670521, 'recall': 0.9366144668158091, 'f1': 0.9218348623853211, 'number': 1341}, 'PER': {'precision': 0.962011771000535, 'recall': 0.9761129207383279, 'f1': 0.9690110482349771, 'number': 1842}, 'overall_precision': 0.9374068554396423, 'overall_recall': 0.9527095254123191, 'overall_f1': 0.944996244053084, 'overall_accuracy': 0.9864013657502796}

## Training procedure

### 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': 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}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1775     | 0.0635          | 0     |
| 0.0470     | 0.0559          | 1     |
| 0.0278     | 0.0603          | 2     |
| 0.0174     | 0.0603          | 3     |
| 0.0124     | 0.0615          | 4     |
| 0.0077     | 0.0722          | 5     |
| 0.0060     | 0.0731          | 6     |
| 0.0038     | 0.0757          | 7     |
| 0.0043     | 0.0731          | 8     |
| 0.0041     | 0.0735          | 9     |
| 0.0019     | 0.0724          | 10    |
| 0.0019     | 0.0786          | 11    |
| 0.0010     | 0.0843          | 12    |
| 0.0008     | 0.0814          | 13    |
| 0.0011     | 0.0867          | 14    |
| 0.0008     | 0.0883          | 15    |
| 0.0005     | 0.0861          | 16    |
| 0.0005     | 0.0869          | 17    |
| 0.0003     | 0.0880          | 18    |
| 0.0003     | 0.0880          | 19    |


### Framework versions

- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3