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# finetuned-ner

This model is a fine-tuned version of [deepset/deberta-v3-base-squad2](https://huggingface.co/deepset/deberta-v3-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4783
- Precision: 0.3264
- Recall: 0.3591
- F1: 0.3420
- Accuracy: 0.8925

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.05

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 39.8167       | 1.0   | 760   | 0.3957          | 0.1844    | 0.2909 | 0.2257 | 0.8499   |
| 21.7333       | 2.0   | 1520  | 0.3853          | 0.2118    | 0.3273 | 0.2571 | 0.8546   |
| 13.8859       | 3.0   | 2280  | 0.3631          | 0.2443    | 0.2909 | 0.2656 | 0.8789   |
| 20.6586       | 4.0   | 3040  | 0.3961          | 0.2946    | 0.3455 | 0.3180 | 0.8753   |
| 13.8654       | 5.0   | 3800  | 0.3821          | 0.2791    | 0.3273 | 0.3013 | 0.8877   |
| 12.6942       | 6.0   | 4560  | 0.4393          | 0.3122    | 0.3364 | 0.3239 | 0.8909   |
| 25.0549       | 7.0   | 5320  | 0.4542          | 0.3106    | 0.3727 | 0.3388 | 0.8824   |
| 5.6816        | 8.0   | 6080  | 0.4432          | 0.2820    | 0.3409 | 0.3086 | 0.8774   |
| 13.1296       | 9.0   | 6840  | 0.4509          | 0.2884    | 0.35   | 0.3162 | 0.8824   |
| 7.7173        | 10.0  | 7600  | 0.4265          | 0.3170    | 0.3818 | 0.3464 | 0.8919   |
| 6.7922        | 11.0  | 8360  | 0.4749          | 0.3320    | 0.3818 | 0.3552 | 0.8892   |
| 5.4287        | 12.0  | 9120  | 0.4564          | 0.2917    | 0.3818 | 0.3307 | 0.8805   |
| 7.4153        | 13.0  | 9880  | 0.4735          | 0.2963    | 0.3273 | 0.3110 | 0.8871   |
| 9.1154        | 14.0  | 10640 | 0.4553          | 0.3416    | 0.3773 | 0.3585 | 0.8894   |
| 5.999         | 15.0  | 11400 | 0.4489          | 0.3203    | 0.4091 | 0.3593 | 0.8880   |
| 9.5128        | 16.0  | 12160 | 0.4947          | 0.3164    | 0.3682 | 0.3403 | 0.8883   |
| 5.6713        | 17.0  | 12920 | 0.4705          | 0.3527    | 0.3864 | 0.3688 | 0.8919   |
| 12.2119       | 18.0  | 13680 | 0.4617          | 0.3123    | 0.3591 | 0.3340 | 0.8857   |
| 8.5658        | 19.0  | 14440 | 0.4764          | 0.3092    | 0.35   | 0.3284 | 0.8944   |
| 11.0664       | 20.0  | 15200 | 0.4557          | 0.3187    | 0.3636 | 0.3397 | 0.8905   |
| 6.7161        | 21.0  | 15960 | 0.4468          | 0.3210    | 0.3955 | 0.3544 | 0.8956   |
| 9.0448        | 22.0  | 16720 | 0.5120          | 0.2872    | 0.3682 | 0.3227 | 0.8792   |
| 6.573         | 23.0  | 17480 | 0.4990          | 0.3307    | 0.3773 | 0.3524 | 0.8869   |
| 5.0543        | 24.0  | 18240 | 0.4763          | 0.3028    | 0.3455 | 0.3227 | 0.8899   |
| 6.8797        | 25.0  | 19000 | 0.4814          | 0.2780    | 0.3273 | 0.3006 | 0.8913   |
| 7.7544        | 26.0  | 19760 | 0.4695          | 0.3024    | 0.3409 | 0.3205 | 0.8946   |
| 4.8346        | 27.0  | 20520 | 0.4849          | 0.3154    | 0.3455 | 0.3297 | 0.8931   |
| 4.4766        | 28.0  | 21280 | 0.4809          | 0.2925    | 0.3364 | 0.3129 | 0.8913   |
| 7.9149        | 29.0  | 22040 | 0.4756          | 0.3238    | 0.3591 | 0.3405 | 0.8930   |
| 7.3033        | 30.0  | 22800 | 0.4783          | 0.3264    | 0.3591 | 0.3420 | 0.8925   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.7.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "finetuned-ner", "results": []}]}
transformers pytorch deberta-v2 token-classification generated_from_trainer license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #token-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

finetuned-ner
=============


This model is a fine-tuned version of deepset/deberta-v3-base-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4783
* Precision: 0.3264
* Recall: 0.3591
* F1: 0.3420
* Accuracy: 0.8925


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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.05


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.7.1
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.05 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.7.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta-v2 #token-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.05 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.7.1
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta-v2 #token-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.05### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.7.1
* Datasets 2.8.0
* Tokenizers 0.13.2