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# distilbert-base-multilingual-cased-WNUT-ner

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3516
- Precision: 0.5497
- Recall: 0.3642
- F1: 0.4381
- Accuracy: 0.9469

## 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  | 0.2727          | 0.6626    | 0.2530 | 0.3662 | 0.9402   |
| No log        | 2.0   | 426  | 0.2636          | 0.5895    | 0.2715 | 0.3718 | 0.9429   |
| 0.1729        | 3.0   | 639  | 0.2933          | 0.5931    | 0.3040 | 0.4020 | 0.9447   |
| 0.1729        | 4.0   | 852  | 0.2861          | 0.5437    | 0.3457 | 0.4227 | 0.9453   |
| 0.0503        | 5.0   | 1065 | 0.3270          | 0.5627    | 0.3494 | 0.4311 | 0.9455   |
| 0.0503        | 6.0   | 1278 | 0.3277          | 0.5451    | 0.3531 | 0.4286 | 0.9463   |
| 0.0503        | 7.0   | 1491 | 0.3471          | 0.5828    | 0.3457 | 0.4340 | 0.9467   |
| 0.0231        | 8.0   | 1704 | 0.3594          | 0.5801    | 0.3457 | 0.4332 | 0.9464   |
| 0.0231        | 9.0   | 1917 | 0.3550          | 0.5567    | 0.3503 | 0.4300 | 0.9467   |
| 0.0121        | 10.0  | 2130 | 0.3516          | 0.5497    | 0.3642 | 0.4381 | 0.9469   |


### 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-multilingual-cased-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": 0.5496503496503496, "name": "Precision"}, {"type": "recall", "value": 0.36422613531047265, "name": "Recall"}, {"type": "f1", "value": 0.4381270903010034, "name": "F1"}, {"type": "accuracy", "value": 0.9468667179618706, "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-multilingual-cased-WNUT-ner
===========================================


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


* Loss: 0.3516
* Precision: 0.5497
* Recall: 0.3642
* F1: 0.4381
* Accuracy: 0.9469


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