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# bert-base-uncased-finetuned-ner

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0712
- Precision: 0.8945
- Recall: 0.9182
- F1: 0.9062
- Accuracy: 0.9793

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- training precision: Mixed Precision

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1318        | 1.0   | 219  | 0.0967          | 0.8371    | 0.8714 | 0.8539 | 0.9705   |
| 0.0597        | 2.0   | 438  | 0.0735          | 0.8912    | 0.9052 | 0.8981 | 0.9779   |
| 0.0523        | 3.0   | 657  | 0.0712          | 0.8945    | 0.9182 | 0.9062 | 0.9793   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner", "results": []}]}
transformers pytorch optimum_graphcore bert token-classification generated_from_trainer dataset:conll2003 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #optimum_graphcore #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-finetuned-ner
===============================


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


* Loss: 0.0712
* Precision: 0.8945
* Recall: 0.9182
* F1: 0.9062
* Accuracy: 0.9793


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: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* training precision: Mixed Precision


### Training results



### Framework versions


* Transformers 4.20.0
* Pytorch 1.10.0+cpu
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* training precision: Mixed Precision ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.10.0+cpu
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #optimum_graphcore #bert #token-classification #generated_from_trainer #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: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* training precision: Mixed Precision ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.10.0+cpu
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #optimum_graphcore #bert #token-classification #generated_from_trainer #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: 1
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: IPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* training precision: Mixed Precision### Training results### Framework versions


* Transformers 4.20.0
* Pytorch 1.10.0+cpu
* Datasets 2.4.0
* Tokenizers 0.12.1