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# just-nce

This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0338
- Able: {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6}
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Ext: {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10}
- Mage: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Ub heading: {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16}
- Overall Precision: 0.6571
- Overall Recall: 0.575
- Overall F1: 0.6133
- Overall Accuracy: 0.68

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Able                                                                     | Eading                                                    | Ext                                                                        | Mage                                                      | Ub heading                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4724        | 14.29 | 100  | 1.0338          | {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16} | 0.6571            | 0.575          | 0.6133     | 0.68             |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "just-nce", "results": []}]}
transformers pytorch tensorboard lilt token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

just-nce
========


This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0338
* Able: {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6}
* Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
* Ext: {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10}
* Mage: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
* Ub heading: {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16}
* Overall Precision: 0.6571
* Overall Recall: 0.575
* Overall F1: 0.6133
* Overall Accuracy: 0.68


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 100


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 100 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 100### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2