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# deberta-v3-large-finetuned-ner

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4130
- Precision: 0.8219
- Recall: 0.8955
- F1: 0.8571
- Accuracy: 0.9310

## 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: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 45   | 1.0375          | 0.4072    | 0.2743 | 0.3278 | 0.7192   |
| No log        | 2.0   | 90   | 0.7673          | 0.4724    | 0.3914 | 0.4281 | 0.7522   |
| No log        | 3.0   | 135  | 0.6973          | 0.4892    | 0.6637 | 0.5633 | 0.7757   |
| No log        | 4.0   | 180  | 0.6645          | 0.5209    | 0.7237 | 0.6058 | 0.7961   |
| No log        | 5.0   | 225  | 0.4692          | 0.6618    | 0.7041 | 0.6823 | 0.8644   |
| No log        | 6.0   | 270  | 0.4469          | 0.6902    | 0.7552 | 0.7213 | 0.8776   |
| No log        | 7.0   | 315  | 0.4761          | 0.6713    | 0.8123 | 0.7351 | 0.8745   |
| No log        | 8.0   | 360  | 0.3956          | 0.7524    | 0.8063 | 0.7784 | 0.9055   |
| No log        | 9.0   | 405  | 0.4272          | 0.7298    | 0.8332 | 0.7781 | 0.8976   |
| No log        | 10.0  | 450  | 0.4285          | 0.7520    | 0.8577 | 0.8014 | 0.9096   |
| No log        | 11.0  | 495  | 0.4022          | 0.7764    | 0.8693 | 0.8202 | 0.9147   |
| 0.4557        | 12.0  | 540  | 0.3584          | 0.8090    | 0.8640 | 0.8356 | 0.9287   |
| 0.4557        | 13.0  | 585  | 0.4022          | 0.8102    | 0.8733 | 0.8405 | 0.9253   |
| 0.4557        | 14.0  | 630  | 0.4149          | 0.8067    | 0.8902 | 0.8464 | 0.9268   |
| 0.4557        | 15.0  | 675  | 0.4160          | 0.8188    | 0.8919 | 0.8538 | 0.9290   |
| 0.4557        | 16.0  | 720  | 0.4015          | 0.8173    | 0.8932 | 0.8536 | 0.9302   |
| 0.4557        | 17.0  | 765  | 0.4084          | 0.8215    | 0.8945 | 0.8565 | 0.9309   |
| 0.4557        | 18.0  | 810  | 0.4133          | 0.8219    | 0.8955 | 0.8571 | 0.9307   |
| 0.4557        | 19.0  | 855  | 0.4131          | 0.8217    | 0.8955 | 0.8570 | 0.9310   |
| 0.4557        | 20.0  | 900  | 0.4130          | 0.8219    | 0.8955 | 0.8571 | 0.9310   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "deberta-v3-large-finetuned-ner", "results": []}]}
transformers pytorch tensorboard deberta-v2 token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

deberta-v3-large-finetuned-ner
==============================


This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4130
* Precision: 0.8219
* Recall: 0.8955
* F1: 0.8571
* Accuracy: 0.9310


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: cosine
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3