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# bert-large-uncased-whole-word-masking-ner-conll2003

This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- Precision: 0.9527
- Recall: 0.9569
- F1: 0.9548
- Accuracy: 0.9887

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4071        | 1.0   | 877  | 0.0584          | 0.9306    | 0.9418 | 0.9362 | 0.9851   |
| 0.0482        | 2.0   | 1754 | 0.0594          | 0.9362    | 0.9491 | 0.9426 | 0.9863   |
| 0.0217        | 3.0   | 2631 | 0.0550          | 0.9479    | 0.9584 | 0.9531 | 0.9885   |
| 0.0103        | 4.0   | 3508 | 0.0592          | 0.9527    | 0.9569 | 0.9548 | 0.9887   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-ner-conll2003", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9886888970085945}}]}]}
transformers pytorch bert token-classification generated_from_trainer en dataset:conll2003 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-large-uncased-whole-word-masking-ner-conll2003
===================================================


This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0592
* Precision: 0.9527
* Recall: 0.9569
* F1: 0.9548
* Accuracy: 0.9887


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


More information needed


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


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3