

# jjglilleberg/bert-finetuned-ner-nbci-disease

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [NCBI Disease Dataset](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/).
It achieves the following results on the evaluation set:
- Precision: 0.759090909090909,
- Recall: 0.8487928843710292,
- F1: 0.8014397120575885,
- Number: 787,
- Overall_precision: 0.759090909090909,
- Overall_recall: 0.8487928843710292,
- Overall_f1: 0.8014397120575885,
- Overall_accuracy: 0.9824785260799204

## Model description

More information needed

## Intended uses & limitations

The intended use of this model is for Disease Name Recognition and Concept Normalization.

## 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': 1020,
      - '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

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1281     | 0.0561          | 0     |
| 0.0372     | 0.0596          | 1     |
| 0.0211     | 0.0645          | 2     |


### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "library_name": "keras", "tags": ["generated_from_keras_callback", "biology", "medical"], "datasets": ["ncbi_disease"], "metrics": ["seqeval"], "pipeline_tag": "token-classification", "model-index": [{"name": "jjglilleberg/bert-finetuned-ner-nbci-disease", "results": []}]}
keras tf bert generated_from_keras_callback biology medical token-classification en dataset:ncbi_disease license:apache-2.0 has_space region:us
TAGS
#keras #tf #bert #generated_from_keras_callback #biology #medical #token-classification #en #dataset-ncbi_disease #license-apache-2.0 #has_space #region-us 

jjglilleberg/bert-finetuned-ner-nbci-disease
============================================


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


* Precision: 0.759090909090909,
* Recall: 0.8487928843710292,
* F1: 0.8014397120575885,
* Number: 787,
* Overall\_precision: 0.759090909090909,
* Overall\_recall: 0.8487928843710292,
* Overall\_f1: 0.8014397120575885,
* Overall\_accuracy: 0.9824785260799204


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


The intended use of this model is for Disease Name Recognition and Concept Normalization.


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': 1020,
			* '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



### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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': 1020,
			* '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 ### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#keras #tf #bert #generated_from_keras_callback #biology #medical #token-classification #en #dataset-ncbi_disease #license-apache-2.0 #has_space #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': 1020,
			* '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 ### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#keras #tf #bert #generated_from_keras_callback #biology #medical #token-classification #en #dataset-ncbi_disease #license-apache-2.0 #has_space #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': 1020,
			* '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### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.3