
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# finetuned_distilbert_fa_zwnj_base_ner

This model is a fine-tuned version of [HooshvareLab/distilbert-fa-zwnj-base](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base) on the mixed NER dataset collected from ARMAN, PEYMA, and WikiANN.
It achieves the following results on the evaluation set:
- Loss: 0.0343
- Precision: 0.9416
- Recall: 0.9549
- F1: 0.9482
- Accuracy: 0.9938

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1456        | 1.0   | 1821  | 0.0699          | 0.7847    | 0.8037 | 0.7941 | 0.9773   |
| 0.0551        | 2.0   | 3642  | 0.0456          | 0.8574    | 0.8875 | 0.8722 | 0.9858   |
| 0.0283        | 3.0   | 5463  | 0.0333          | 0.8957    | 0.9225 | 0.9089 | 0.9902   |
| 0.0161        | 4.0   | 7284  | 0.0299          | 0.9229    | 0.9374 | 0.9301 | 0.9921   |
| 0.0103        | 5.0   | 9105  | 0.0298          | 0.9314    | 0.9471 | 0.9392 | 0.9929   |
| 0.0069        | 6.0   | 10926 | 0.0323          | 0.9305    | 0.9513 | 0.9408 | 0.9930   |
| 0.0045        | 7.0   | 12747 | 0.0337          | 0.9363    | 0.9510 | 0.9436 | 0.9933   |
| 0.0031        | 8.0   | 14568 | 0.0339          | 0.9395    | 0.9526 | 0.9460 | 0.9937   |
| 0.0024        | 9.0   | 16389 | 0.0334          | 0.9392    | 0.9545 | 0.9468 | 0.9938   |
| 0.0017        | 10.0  | 18210 | 0.0343          | 0.9416    | 0.9549 | 0.9482 | 0.9938   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "finetuned_distilbert_fa_zwnj_base_ner", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

finetuned\_distilbert\_fa\_zwnj\_base\_ner
==========================================


This model is a fine-tuned version of HooshvareLab/distilbert-fa-zwnj-base on the mixed NER dataset collected from ARMAN, PEYMA, and WikiANN.
It achieves the following results on the evaluation set:


* Loss: 0.0343
* Precision: 0.9416
* Recall: 0.9549
* F1: 0.9482
* Accuracy: 0.9938


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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1