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# bert-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7230

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 68   | 7.5711          |
| No log        | 2.0   | 136  | 7.5137          |
| No log        | 3.0   | 204  | 7.4045          |
| No log        | 4.0   | 272  | 7.2699          |
| No log        | 5.0   | 340  | 7.1041          |
| No log        | 6.0   | 408  | 6.9418          |
| No log        | 7.0   | 476  | 6.7958          |
| 7.5401        | 8.0   | 544  | 6.6459          |
| 7.5401        | 9.0   | 612  | 6.5085          |
| 7.5401        | 10.0  | 680  | 6.3777          |
| 7.5401        | 11.0  | 748  | 6.2365          |
| 7.5401        | 12.0  | 816  | 6.1106          |
| 7.5401        | 13.0  | 884  | 5.9833          |
| 7.5401        | 14.0  | 952  | 5.8674          |
| 6.5715        | 15.0  | 1020 | 5.7555          |
| 6.5715        | 16.0  | 1088 | 5.6403          |
| 6.5715        | 17.0  | 1156 | 5.5376          |
| 6.5715        | 18.0  | 1224 | 5.4137          |
| 6.5715        | 19.0  | 1292 | 5.3225          |
| 6.5715        | 20.0  | 1360 | 5.2182          |
| 6.5715        | 21.0  | 1428 | 5.1122          |
| 6.5715        | 22.0  | 1496 | 5.0065          |
| 5.7874        | 23.0  | 1564 | 4.9041          |
| 5.7874        | 24.0  | 1632 | 4.8166          |
| 5.7874        | 25.0  | 1700 | 4.7134          |
| 5.7874        | 26.0  | 1768 | 4.6366          |
| 5.7874        | 27.0  | 1836 | 4.5368          |
| 5.7874        | 28.0  | 1904 | 4.4495          |
| 5.7874        | 29.0  | 1972 | 4.3610          |
| 5.0922        | 30.0  | 2040 | 4.2840          |
| 5.0922        | 31.0  | 2108 | 4.1986          |
| 5.0922        | 32.0  | 2176 | 4.1160          |
| 5.0922        | 33.0  | 2244 | 4.0367          |
| 5.0922        | 34.0  | 2312 | 3.9648          |
| 5.0922        | 35.0  | 2380 | 3.8908          |
| 5.0922        | 36.0  | 2448 | 3.8100          |
| 4.4927        | 37.0  | 2516 | 3.7385          |
| 4.4927        | 38.0  | 2584 | 3.6692          |
| 4.4927        | 39.0  | 2652 | 3.6037          |
| 4.4927        | 40.0  | 2720 | 3.5427          |
| 4.4927        | 41.0  | 2788 | 3.4718          |
| 4.4927        | 42.0  | 2856 | 3.4000          |
| 4.4927        | 43.0  | 2924 | 3.3363          |
| 4.4927        | 44.0  | 2992 | 3.2797          |
| 3.9767        | 45.0  | 3060 | 3.2366          |
| 3.9767        | 46.0  | 3128 | 3.1579          |
| 3.9767        | 47.0  | 3196 | 3.0965          |
| 3.9767        | 48.0  | 3264 | 3.0387          |
| 3.9767        | 49.0  | 3332 | 2.9887          |
| 3.9767        | 50.0  | 3400 | 2.9314          |
| 3.9767        | 51.0  | 3468 | 2.8779          |
| 3.5181        | 52.0  | 3536 | 2.8385          |
| 3.5181        | 53.0  | 3604 | 2.7807          |
| 3.5181        | 54.0  | 3672 | 2.7384          |
| 3.5181        | 55.0  | 3740 | 2.6938          |
| 3.5181        | 56.0  | 3808 | 2.6386          |
| 3.5181        | 57.0  | 3876 | 2.6043          |
| 3.5181        | 58.0  | 3944 | 2.5500          |
| 3.1415        | 59.0  | 4012 | 2.5146          |
| 3.1415        | 60.0  | 4080 | 2.4785          |
| 3.1415        | 61.0  | 4148 | 2.4321          |
| 3.1415        | 62.0  | 4216 | 2.3939          |
| 3.1415        | 63.0  | 4284 | 2.3641          |
| 3.1415        | 64.0  | 4352 | 2.3193          |
| 3.1415        | 65.0  | 4420 | 2.2894          |
| 3.1415        | 66.0  | 4488 | 2.2563          |
| 2.8316        | 67.0  | 4556 | 2.2242          |
| 2.8316        | 68.0  | 4624 | 2.1952          |
| 2.8316        | 69.0  | 4692 | 2.1640          |
| 2.8316        | 70.0  | 4760 | 2.1346          |
| 2.8316        | 71.0  | 4828 | 2.1069          |
| 2.8316        | 72.0  | 4896 | 2.0837          |
| 2.8316        | 73.0  | 4964 | 2.0536          |
| 2.5874        | 74.0  | 5032 | 2.0310          |
| 2.5874        | 75.0  | 5100 | 2.0053          |
| 2.5874        | 76.0  | 5168 | 1.9829          |
| 2.5874        | 77.0  | 5236 | 1.9605          |
| 2.5874        | 78.0  | 5304 | 1.9421          |
| 2.5874        | 79.0  | 5372 | 1.9192          |
| 2.5874        | 80.0  | 5440 | 1.9045          |
| 2.3824        | 81.0  | 5508 | 1.8918          |
| 2.3824        | 82.0  | 5576 | 1.8708          |
| 2.3824        | 83.0  | 5644 | 1.8547          |
| 2.3824        | 84.0  | 5712 | 1.8397          |
| 2.3824        | 85.0  | 5780 | 1.8275          |
| 2.3824        | 86.0  | 5848 | 1.8078          |
| 2.3824        | 87.0  | 5916 | 1.8017          |
| 2.3824        | 88.0  | 5984 | 1.7901          |
| 2.2537        | 89.0  | 6052 | 1.7802          |
| 2.2537        | 90.0  | 6120 | 1.7678          |
| 2.2537        | 91.0  | 6188 | 1.7610          |
| 2.2537        | 92.0  | 6256 | 1.7523          |
| 2.2537        | 93.0  | 6324 | 1.7447          |
| 2.2537        | 94.0  | 6392 | 1.7385          |
| 2.2537        | 95.0  | 6460 | 1.7343          |
| 2.1756        | 96.0  | 6528 | 1.7286          |
| 2.1756        | 97.0  | 6596 | 1.7267          |
| 2.1756        | 98.0  | 6664 | 1.7239          |
| 2.1756        | 99.0  | 6732 | 1.7233          |
| 2.1756        | 100.0 | 6800 | 1.7230          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-ner
==================


This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7230


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2