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# bert-finetuned-ner-10epochs

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: 0.0895
- Precision: 0.9167
- Recall: 0.9546
- F1: 0.9352
- Accuracy: 0.9888

## 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0061        | 1.0   | 2261  | 0.0961          | 0.8813    | 0.9436 | 0.9114 | 0.9869   |
| 0.0241        | 2.0   | 4522  | 0.0566          | 0.9001    | 0.9502 | 0.9245 | 0.9878   |
| 0.02          | 3.0   | 6783  | 0.0560          | 0.9010    | 0.9528 | 0.9261 | 0.9879   |
| 0.0169        | 4.0   | 9044  | 0.0519          | 0.9045    | 0.9539 | 0.9285 | 0.9884   |
| 0.0129        | 5.0   | 11305 | 0.0621          | 0.9073    | 0.9568 | 0.9314 | 0.9886   |
| 0.009         | 6.0   | 13566 | 0.0623          | 0.9123    | 0.9451 | 0.9284 | 0.9883   |
| 0.0078        | 7.0   | 15827 | 0.0727          | 0.9145    | 0.9473 | 0.9306 | 0.9886   |
| 0.0056        | 8.0   | 18088 | 0.0806          | 0.9134    | 0.9535 | 0.9330 | 0.9882   |
| 0.0034        | 9.0   | 20349 | 0.0856          | 0.9103    | 0.9546 | 0.9319 | 0.9886   |
| 0.003         | 10.0  | 22610 | 0.0895          | 0.9167    | 0.9546 | 0.9352 | 0.9888   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner-10epochs", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-ner-10epochs
===========================


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: 0.0895
* Precision: 0.9167
* Recall: 0.9546
* F1: 0.9352
* Accuracy: 0.9888


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: 10


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 10 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 10 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3