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# Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5062
- Precision: 0.6603
- Recall: 0.4682
- F1: 0.5479
- Accuracy: 0.9355

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 107  | 0.3396          | 0.6470    | 0.4269 | 0.5144 | 0.9330   |
| No log        | 2.0   | 214  | 0.3475          | 0.5948    | 0.4539 | 0.5149 | 0.9335   |
| No log        | 3.0   | 321  | 0.3793          | 0.6613    | 0.4253 | 0.5177 | 0.9332   |
| No log        | 4.0   | 428  | 0.3598          | 0.6195    | 0.4944 | 0.5500 | 0.9354   |
| 0.0409        | 5.0   | 535  | 0.3702          | 0.5802    | 0.4571 | 0.5113 | 0.9308   |
| 0.0409        | 6.0   | 642  | 0.4192          | 0.6546    | 0.4459 | 0.5305 | 0.9344   |
| 0.0409        | 7.0   | 749  | 0.4039          | 0.6360    | 0.4610 | 0.5346 | 0.9354   |
| 0.0409        | 8.0   | 856  | 0.4104          | 0.6564    | 0.4587 | 0.5400 | 0.9353   |
| 0.0409        | 9.0   | 963  | 0.3839          | 0.6283    | 0.4944 | 0.5534 | 0.9361   |
| 0.0132        | 10.0  | 1070 | 0.4331          | 0.6197    | 0.4547 | 0.5245 | 0.9339   |
| 0.0132        | 11.0  | 1177 | 0.4152          | 0.6196    | 0.4817 | 0.5420 | 0.9355   |
| 0.0132        | 12.0  | 1284 | 0.4654          | 0.6923    | 0.4507 | 0.5460 | 0.9353   |
| 0.0132        | 13.0  | 1391 | 0.4869          | 0.6739    | 0.4436 | 0.5350 | 0.9350   |
| 0.0132        | 14.0  | 1498 | 0.4297          | 0.6424    | 0.4769 | 0.5474 | 0.9353   |
| 0.0061        | 15.0  | 1605 | 0.4507          | 0.6272    | 0.4626 | 0.5325 | 0.9340   |
| 0.0061        | 16.0  | 1712 | 0.4410          | 0.6066    | 0.4793 | 0.5355 | 0.9335   |
| 0.0061        | 17.0  | 1819 | 0.4851          | 0.6639    | 0.4523 | 0.5381 | 0.9351   |
| 0.0061        | 18.0  | 1926 | 0.4815          | 0.6553    | 0.4563 | 0.5380 | 0.9346   |
| 0.0035        | 19.0  | 2033 | 0.5188          | 0.6780    | 0.4420 | 0.5351 | 0.9350   |
| 0.0035        | 20.0  | 2140 | 0.4986          | 0.6770    | 0.4698 | 0.5547 | 0.9363   |
| 0.0035        | 21.0  | 2247 | 0.4834          | 0.6552    | 0.4714 | 0.5483 | 0.9355   |
| 0.0035        | 22.0  | 2354 | 0.5094          | 0.6784    | 0.4595 | 0.5479 | 0.9358   |
| 0.0035        | 23.0  | 2461 | 0.4954          | 0.6583    | 0.4579 | 0.5401 | 0.9354   |
| 0.0026        | 24.0  | 2568 | 0.5035          | 0.6667    | 0.4595 | 0.5440 | 0.9354   |
| 0.0026        | 25.0  | 2675 | 0.5000          | 0.6599    | 0.4658 | 0.5461 | 0.9355   |
| 0.0026        | 26.0  | 2782 | 0.4968          | 0.6697    | 0.4738 | 0.5549 | 0.9357   |
| 0.0026        | 27.0  | 2889 | 0.4991          | 0.6545    | 0.4714 | 0.5481 | 0.9352   |
| 0.0026        | 28.0  | 2996 | 0.4936          | 0.6508    | 0.4769 | 0.5505 | 0.9353   |
| 0.0021        | 29.0  | 3103 | 0.5005          | 0.6535    | 0.4722 | 0.5482 | 0.9353   |
| 0.0021        | 30.0  | 3210 | 0.5062          | 0.6603    | 0.4682 | 0.5479 | 0.9355   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "args": "wnut_17"}, "metrics": [{"type": "precision", "value": 0.6603139013452914, "name": "Precision"}, {"type": "recall", "value": 0.4682034976152623, "name": "Recall"}, {"type": "f1", "value": 0.547906976744186, "name": "F1"}, {"type": "accuracy", "value": 0.9355430668654662, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer dataset:wnut_17 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
====================================================


This model is a fine-tuned version of distilbert-base-uncased on the wnut\_17 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5062
* Precision: 0.6603
* Recall: 0.4682
* F1: 0.5479
* Accuracy: 0.9355


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1