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# DeBERTa-finetuned-ner-S800

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.6730
- Recall: 0.7899
- F1: 0.7268
- Accuracy: 0.9783

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 55   | 0.0744          | 0.5840    | 0.6527 | 0.6164 | 0.9703   |
| No log        | 2.0   | 110  | 0.0639          | 0.6332    | 0.7689 | 0.6945 | 0.9764   |
| No log        | 3.0   | 165  | 0.0585          | 0.6424    | 0.7801 | 0.7046 | 0.9766   |
| No log        | 4.0   | 220  | 0.0581          | 0.6754    | 0.7955 | 0.7305 | 0.9785   |
| No log        | 5.0   | 275  | 0.0606          | 0.6730    | 0.7899 | 0.7268 | 0.9783   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "DeBERTa-finetuned-ner-S800", "results": []}]}
transformers pytorch tensorboard deberta-v2 token-classification generated_from_trainer base_model:microsoft/deberta-v3-base license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us 

DeBERTa-finetuned-ner-S800
==========================


This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0606
* Precision: 0.6730
* Recall: 0.7899
* F1: 0.7268
* Accuracy: 0.9783


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: 5


### Training results



### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* 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: 5 ### Training results ### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #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: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3