
<!-- 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. -->

# tiny-vanilla-target-conll2003

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1431
- Precision: 0.7507
- Recall: 0.8177
- F1: 0.7828
- Accuracy: 0.9581

## 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: 3e-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: constant
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7673        | 1.14  | 500  | 0.4291          | 0.4793    | 0.5160 | 0.4970 | 0.8920   |
| 0.3746        | 2.28  | 1000 | 0.2869          | 0.5976    | 0.6572 | 0.6260 | 0.9256   |
| 0.2869        | 3.42  | 1500 | 0.2292          | 0.6411    | 0.7184 | 0.6776 | 0.9370   |
| 0.236         | 4.56  | 2000 | 0.1988          | 0.6805    | 0.7516 | 0.7143 | 0.9438   |
| 0.2026        | 5.69  | 2500 | 0.1772          | 0.7047    | 0.7718 | 0.7367 | 0.9482   |
| 0.1798        | 6.83  | 3000 | 0.1649          | 0.7179    | 0.7864 | 0.7506 | 0.9514   |
| 0.158         | 7.97  | 3500 | 0.1559          | 0.7256    | 0.7987 | 0.7604 | 0.9543   |
| 0.1415        | 9.11  | 4000 | 0.1500          | 0.7379    | 0.8034 | 0.7693 | 0.9563   |
| 0.127         | 10.25 | 4500 | 0.1462          | 0.7532    | 0.8134 | 0.7821 | 0.9573   |
| 0.1173        | 11.39 | 5000 | 0.1431          | 0.7507    | 0.8177 | 0.7828 | 0.9581   |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny-vanilla-target-conll2003", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

tiny-vanilla-target-conll2003
=============================


This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1431
* Precision: 0.7507
* Recall: 0.8177
* F1: 0.7828
* Accuracy: 0.9581


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: 3e-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: constant
* training\_steps: 5000


### Training results



### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: constant
* training\_steps: 5000 ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: constant
* training\_steps: 5000 ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: constant
* training\_steps: 5000### Training results### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2