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# bert-base-NER-finetuned-ner-cerec

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1511
- Precision: 0.8652
- Recall: 0.8531
- F1: 0.8592
- Accuracy: 0.9790

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 45   | 0.4929          | 0.6532    | 0.5664 | 0.6067 | 0.9237   |
| No log        | 2.0   | 90   | 0.3090          | 0.8145    | 0.7063 | 0.7566 | 0.9497   |
| No log        | 3.0   | 135  | 0.2477          | 0.8433    | 0.7902 | 0.8159 | 0.9625   |
| No log        | 4.0   | 180  | 0.2209          | 0.8169    | 0.8112 | 0.8140 | 0.9669   |
| No log        | 5.0   | 225  | 0.1906          | 0.8369    | 0.8252 | 0.8310 | 0.9739   |
| No log        | 6.0   | 270  | 0.1568          | 0.8662    | 0.8601 | 0.8632 | 0.9784   |
| No log        | 7.0   | 315  | 0.1532          | 0.8732    | 0.8671 | 0.8702 | 0.9790   |
| No log        | 8.0   | 360  | 0.1530          | 0.8671    | 0.8671 | 0.8671 | 0.9790   |
| No log        | 9.0   | 405  | 0.1526          | 0.8723    | 0.8601 | 0.8662 | 0.9790   |
| No log        | 10.0  | 450  | 0.1511          | 0.8652    | 0.8531 | 0.8592 | 0.9790   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.11.0

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

bert-base-NER-finetuned-ner-cerec
=================================


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


* Loss: 0.1511
* Precision: 0.8652
* Recall: 0.8531
* F1: 0.8592
* Accuracy: 0.9790


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: 16
* eval\_batch\_size: 16
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.10.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.10.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.10.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.24.0
* Pytorch 1.12.1
* Datasets 2.10.1
* Tokenizers 0.11.0