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# 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
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_keras_callback"], "datasets": ["wikiann", "BeardedJohn/NERPERDemo", "BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2"], "model-index": [{"name": "bert-finetuned-ner-per-v2", "results": []}]}
transformers tf bert token-classification generated_from_keras_callback en dataset:wikiann dataset:BeardedJohn/NERPERDemo dataset:BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #en #dataset-wikiann #dataset-BeardedJohn/NERPERDemo #dataset-BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-ner-per-v2
=========================


This model is a fine-tuned version of BERT on three datasets:


* conll-endava mixed dataset, second version
* NERPERDemo dataset
* 12000 instances of the wikiann, english version dataset.


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 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.


### 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:



On NERPERDemo:



On wikiann:



### Framework versions


* Transformers 4.25.1
* TensorFlow 2.9.2
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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:



On NERPERDemo:



On wikiann: ### Framework versions


* Transformers 4.25.1
* TensorFlow 2.9.2
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #en #dataset-wikiann #dataset-BeardedJohn/NERPERDemo #dataset-BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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:



On NERPERDemo:



On wikiann: ### Framework versions


* Transformers 4.25.1
* TensorFlow 2.9.2
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #en #dataset-wikiann #dataset-BeardedJohn/NERPERDemo #dataset-BeardedJohn/ubb-endava-conll-assistant-ner-only-misc-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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:



On NERPERDemo:



On wikiann:### Framework versions


* Transformers 4.25.1
* TensorFlow 2.9.2
* Datasets 2.8.0
* Tokenizers 0.13.2