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# small-vanilla-target-conll2003

This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1128
- Precision: 0.8947
- Recall: 0.9165
- F1: 0.9055
- Accuracy: 0.9782

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.217         | 1.14  | 500  | 0.0895          | 0.8414    | 0.8846 | 0.8624 | 0.9725   |
| 0.0723        | 2.28  | 1000 | 0.0808          | 0.8853    | 0.8975 | 0.8914 | 0.9762   |
| 0.0427        | 3.42  | 1500 | 0.0807          | 0.8886    | 0.9118 | 0.9001 | 0.9778   |
| 0.0274        | 4.56  | 2000 | 0.0816          | 0.8983    | 0.9142 | 0.9062 | 0.9790   |
| 0.0186        | 5.69  | 2500 | 0.0910          | 0.8990    | 0.9201 | 0.9094 | 0.9788   |
| 0.0132        | 6.83  | 3000 | 0.0933          | 0.8978    | 0.9180 | 0.9078 | 0.9789   |
| 0.0097        | 7.97  | 3500 | 0.0954          | 0.9025    | 0.9211 | 0.9117 | 0.9798   |
| 0.0076        | 9.11  | 4000 | 0.1003          | 0.9007    | 0.9221 | 0.9113 | 0.9794   |
| 0.0058        | 10.25 | 4500 | 0.1037          | 0.9032    | 0.9222 | 0.9126 | 0.9798   |
| 0.005         | 11.39 | 5000 | 0.1128          | 0.8947    | 0.9165 | 0.9055 | 0.9782   |


### 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": "small-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 

small-vanilla-target-conll2003
==============================


This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1128
* Precision: 0.8947
* Recall: 0.9165
* F1: 0.9055
* Accuracy: 0.9782


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