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# bert-to-distilbert-NER

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 44.0386
- Precision: 0.0145
- Recall: 0.0185
- F1: 0.0163
- Accuracy: 0.7597

## 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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 201.4012      | 1.0   | 110  | 133.7231        | 0.0153    | 0.0106 | 0.0125 | 0.7539   |
| 106.9317      | 2.0   | 220  | 99.3629         | 0.0266    | 0.0305 | 0.0284 | 0.7593   |
| 81.3601       | 3.0   | 330  | 80.3763         | 0.0159    | 0.0214 | 0.0183 | 0.7604   |
| 63.8325       | 4.0   | 440  | 67.7620         | 0.0179    | 0.0244 | 0.0207 | 0.7599   |
| 52.0271       | 5.0   | 550  | 59.0806         | 0.0203    | 0.0268 | 0.0231 | 0.7598   |
| 44.4419       | 6.0   | 660  | 55.3208         | 0.0211    | 0.0278 | 0.0240 | 0.7603   |
| 39.2351       | 7.0   | 770  | 52.4510         | 0.0170    | 0.0222 | 0.0193 | 0.7598   |
| 35.3438       | 8.0   | 880  | 50.4576         | 0.0205    | 0.0268 | 0.0232 | 0.7604   |
| 32.7385       | 9.0   | 990  | 48.3418         | 0.0173    | 0.0227 | 0.0197 | 0.7595   |
| 30.6531       | 10.0  | 1100 | 46.7304         | 0.0147    | 0.0188 | 0.0165 | 0.7600   |
| 29.0811       | 11.0  | 1210 | 46.3386         | 0.0151    | 0.0190 | 0.0168 | 0.7599   |
| 27.9501       | 12.0  | 1320 | 45.4516         | 0.0163    | 0.0204 | 0.0181 | 0.7604   |
| 26.7452       | 13.0  | 1430 | 44.3425         | 0.0154    | 0.0199 | 0.0173 | 0.7592   |
| 25.5367       | 14.0  | 1540 | 44.0415         | 0.0146    | 0.0190 | 0.0165 | 0.7594   |
| 24.5507       | 15.0  | 1650 | 44.0386         | 0.0145    | 0.0185 | 0.0163 | 0.7597   |


### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-to-distilbert-NER", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.014488935721812434, "name": "Precision"}, {"type": "recall", "value": 0.018512285425782565, "name": "Recall"}, {"type": "f1", "value": 0.016255356878971478, "name": "F1"}, {"type": "accuracy", "value": 0.7597280273150055, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:conll2003 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-to-distilbert-NER
======================


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


* Loss: 44.0386
* Precision: 0.0145
* Recall: 0.0185
* F1: 0.0163
* Accuracy: 0.7597


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: 6e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1