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# bert-finetuned-ner

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1622
- Precision: 0.7774
- Recall: 0.7937
- F1: 0.7854
- Accuracy: 0.9707

## 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: 8e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 131  | 0.1355          | 0.6880    | 0.7298 | 0.7083 | 0.9604   |
| No log        | 2.0   | 262  | 0.1194          | 0.7564    | 0.7727 | 0.7645 | 0.9684   |
| No log        | 3.0   | 393  | 0.1277          | 0.7731    | 0.7868 | 0.7799 | 0.9691   |
| 0.0433        | 4.0   | 524  | 0.1433          | 0.7553    | 0.7829 | 0.7688 | 0.9685   |
| 0.0433        | 5.0   | 655  | 0.1515          | 0.7734    | 0.7946 | 0.7839 | 0.9700   |
| 0.0433        | 6.0   | 786  | 0.1518          | 0.7819    | 0.8008 | 0.7912 | 0.9708   |
| 0.0433        | 7.0   | 917  | 0.1602          | 0.7752    | 0.7914 | 0.7832 | 0.9704   |
| 0.0094        | 8.0   | 1048 | 0.1622          | 0.7774    | 0.7937 | 0.7854 | 0.9707   |


### 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", "results": []}]}
transformers pytorch distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #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 distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1622
* Precision: 0.7774
* Recall: 0.7937
* F1: 0.7854
* Accuracy: 0.9707


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: 8e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### 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: 8e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #distilbert #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: 8e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


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