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# Bertweet-base finetuned on wnut17_ner

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the [wnut_17](https://huggingface.co/datasets/wnut_17) dataset.

It achieves the following results on the evaluation set:
- Loss: 0.3376
- Overall Precision: 0.6803
- Overall Recall: 0.6096
- Overall F1: 0.6430
- Overall Accuracy: 0.9509
- Corporation F1: 0.2975
- Creative-work F1: 0.4436
- Group F1: 0.3624
- Location F1: 0.6834
- Person F1: 0.7902
- Product F1: 0.3887

## 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: 1e-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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Corporation F1 | Creative-work F1 | Group F1 | Location F1 | Person F1 | Product F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:--------:|:-----------:|:---------:|:----------:|
| 0.0215        | 1.0   | 213  | 0.2913          | 0.7026            | 0.5905         | 0.6417     | 0.9507           | 0.2832         | 0.4444           | 0.2975   | 0.6854      | 0.7788    | 0.4015     |
| 0.0213        | 2.0   | 426  | 0.3052          | 0.6774            | 0.5772         | 0.6233     | 0.9495           | 0.2830         | 0.3483           | 0.3231   | 0.6857      | 0.7728    | 0.3794     |
| 0.0288        | 3.0   | 639  | 0.3378          | 0.7061            | 0.5507         | 0.6188     | 0.9467           | 0.3077         | 0.4184           | 0.3529   | 0.6222      | 0.7532    | 0.3910     |
| 0.0124        | 4.0   | 852  | 0.2712          | 0.6574            | 0.6121         | 0.6340     | 0.9502           | 0.3077         | 0.4842           | 0.3167   | 0.6809      | 0.7735    | 0.3986     |
| 0.0208        | 5.0   | 1065 | 0.2905          | 0.7108            | 0.6063         | 0.6544     | 0.9518           | 0.3063         | 0.4286           | 0.3419   | 0.7052      | 0.7913    | 0.4223     |
| 0.0071        | 6.0   | 1278 | 0.3189          | 0.6756            | 0.5847         | 0.6269     | 0.9494           | 0.2759         | 0.4380           | 0.3256   | 0.6744      | 0.7781    | 0.3779     |
| 0.0073        | 7.0   | 1491 | 0.3593          | 0.7330            | 0.5540         | 0.6310     | 0.9476           | 0.3061         | 0.4388           | 0.3784   | 0.6946      | 0.7631    | 0.3374     |
| 0.0135        | 8.0   | 1704 | 0.3564          | 0.6875            | 0.5482         | 0.6100     | 0.9471           | 0.34           | 0.4179           | 0.3088   | 0.6632      | 0.7486    | 0.3695     |
| 0.0097        | 9.0   | 1917 | 0.3085          | 0.6598            | 0.6395         | 0.6495     | 0.9516           | 0.3111         | 0.4609           | 0.3836   | 0.7090      | 0.7906    | 0.4083     |
| 0.0108        | 10.0  | 2130 | 0.3045          | 0.6605            | 0.6478         | 0.6541     | 0.9509           | 0.3529         | 0.4580           | 0.3649   | 0.6897      | 0.7843    | 0.4387     |
| 0.013         | 11.0  | 2343 | 0.3383          | 0.6788            | 0.6179         | 0.6470     | 0.9507           | 0.2783         | 0.4248           | 0.3358   | 0.7368      | 0.7958    | 0.3655     |
| 0.0076        | 12.0  | 2556 | 0.3617          | 0.6920            | 0.5523         | 0.6143     | 0.9474           | 0.2708         | 0.3985           | 0.3333   | 0.6740      | 0.7566    | 0.3525     |
| 0.0042        | 13.0  | 2769 | 0.3747          | 0.6896            | 0.5664         | 0.6220     | 0.9473           | 0.2478         | 0.3915           | 0.3521   | 0.6561      | 0.7742    | 0.3539     |
| 0.0049        | 14.0  | 2982 | 0.3376          | 0.6803            | 0.6096         | 0.6430     | 0.9509           | 0.2975         | 0.4436           | 0.3624   | 0.6834      | 0.7902    | 0.3887     |


### Overall results

|        metric_type |      train | validation |       test |
|:-------------------|-----------:|-----------:|-----------:|
| loss               | 0.012030   | 0.271155   | 0.273943   |
| runtime            | 16.292400  | 5.068800   | 8.596800   |
| samples_per_second | 208.318000 | 199.060000 | 149.707000 |
| steps_per_second   | 13.074000  | 12.626000  | 9.422000   |
| corporation_f1     | 0.936877   | 0.307692   | 0.368627   |
| person_f1          | 0.984252   | 0.773455   | 0.689826   |
| product_f1         | 0.893246   | 0.398625   | 0.270423   |
| creative-work_f1   | 0.880562   | 0.484211   | 0.415274   |
| group_f1           | 0.975547   | 0.316667   | 0.411348   |
| location_f1        | 0.978887   | 0.680851   | 0.638695   |
| overall_accuracy   | 0.997709   | 0.950244   | 0.949920   |
| overall_f1         | 0.961113   | 0.633978   | 0.550816   |
| overall_precision  | 0.956337   | 0.657449   | 0.615483   |
| overall_recall     | 0.965938   | 0.612126   | 0.498446   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer", "named-entity-recognition", "token-classification"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "vinai/bertweet-base", "model-index": [{"name": "fine_tune_bertweet-base-lp-ft", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "args": "semval"}, "metrics": [{"type": "precision", "value": 0.6154830454254638, "name": "Precision"}, {"type": "recall", "value": 0.49844559585492226, "name": "Recall"}, {"type": "f1", "value": 0.5508159175493844, "name": "F1"}, {"type": "accuracy", "value": 0.9499198834668608, "name": "Accuracy"}]}]}]}
transformers pytorch safetensors roberta token-classification generated_from_trainer named-entity-recognition dataset:wnut_17 base_model:vinai/bertweet-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #named-entity-recognition #dataset-wnut_17 #base_model-vinai/bertweet-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

Bertweet-base finetuned on wnut17\_ner
======================================


This model is a fine-tuned version of vinai/bertweet-base on the wnut\_17 dataset.


It achieves the following results on the evaluation set:


* Loss: 0.3376
* Overall Precision: 0.6803
* Overall Recall: 0.6096
* Overall F1: 0.6430
* Overall Accuracy: 0.9509
* Corporation F1: 0.2975
* Creative-work F1: 0.4436
* Group F1: 0.3624
* Location F1: 0.6834
* Person F1: 0.7902
* Product F1: 0.3887


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: 1e-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: 100


### Training results



### Overall results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 100 ### Training results ### Overall results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #named-entity-recognition #dataset-wnut_17 #base_model-vinai/bertweet-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 100 ### Training results ### Overall results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #named-entity-recognition #dataset-wnut_17 #base_model-vinai/bertweet-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 100### Training results### Overall results### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.0.0
* Tokenizers 0.11.6