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# bert-base-uncased-spam-classifier

This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0895
- Accuracy: 0.9892
- Precision: 0.9580
- Recall: 0.9580
- F1: 0.9580

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0858        | 1.0   | 1115 | 0.0873          | 0.9883   | 0.9577    | 0.9510 | 0.9544 |
| 0.0503        | 2.0   | 2230 | 0.0628          | 0.9910   | 0.9784    | 0.9510 | 0.9645 |
| 0.0249        | 3.0   | 3345 | 0.0939          | 0.9892   | 0.9712    | 0.9441 | 0.9574 |
| 0.0083        | 4.0   | 4460 | 0.0955          | 0.9892   | 0.9580    | 0.9580 | 0.9580 |
| 0.0           | 5.0   | 5575 | 0.0927          | 0.9892   | 0.9580    | 0.9580 | 0.9580 |
| 0.0           | 6.0   | 6690 | 0.0895          | 0.9892   | 0.9580    | 0.9580 | 0.9580 |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-base-uncased-spam-classifier", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-spam-classifier
=================================


This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0895
* Accuracy: 0.9892
* Precision: 0.9580
* Recall: 0.9580
* F1: 0.9580


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3