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# distilbert-base-uncased-WNUT-ner

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: 0.3510
- Precision: 0.5362
- Recall: 0.3911
- F1: 0.4523
- Accuracy: 0.9465

## 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   | 213  | 0.2745          | 0.5099    | 0.2614 | 0.3456 | 0.9392   |
| No log        | 2.0   | 426  | 0.2729          | 0.6080    | 0.3105 | 0.4110 | 0.9436   |
| 0.1878        | 3.0   | 639  | 0.2826          | 0.5769    | 0.3373 | 0.4257 | 0.9456   |
| 0.1878        | 4.0   | 852  | 0.2840          | 0.5440    | 0.3726 | 0.4422 | 0.9461   |
| 0.0465        | 5.0   | 1065 | 0.3286          | 0.5914    | 0.3568 | 0.4451 | 0.9461   |
| 0.0465        | 6.0   | 1278 | 0.3273          | 0.5693    | 0.3577 | 0.4394 | 0.9466   |
| 0.0465        | 7.0   | 1491 | 0.3462          | 0.5621    | 0.3818 | 0.4547 | 0.9469   |
| 0.0195        | 8.0   | 1704 | 0.3576          | 0.5442    | 0.3707 | 0.4410 | 0.9461   |
| 0.0195        | 9.0   | 1917 | 0.3563          | 0.5306    | 0.3698 | 0.4358 | 0.9460   |
| 0.0111        | 10.0  | 2130 | 0.3510          | 0.5362    | 0.3911 | 0.4523 | 0.9465   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-WNUT-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "config": "wnut_17", "split": "test", "args": "wnut_17"}, "metrics": [{"type": "precision", "value": 0.5362134688691232, "name": "Precision"}, {"type": "recall", "value": 0.3911028730305839, "name": "Recall"}, {"type": "f1", "value": 0.45230439442658094, "name": "F1"}, {"type": "accuracy", "value": 0.9465178914967295, "name": "Accuracy"}]}]}]}
transformers pytorch distilbert token-classification generated_from_trainer dataset:wnut_17 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-WNUT-ner
================================


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: 0.3510
* Precision: 0.5362
* Recall: 0.3911
* F1: 0.4523
* Accuracy: 0.9465


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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 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 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2