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# Manirathinam21/DistilBert_SMSSpam_classifier

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an SMSSpam Detection dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0114
- Train Accuracy: 0.9962
- Epoch: 2

## Target Labels

label: a classification label, with possible values including 
- Ham : 0 
- Spam : 1 

## Model description

Tokenizer used is DistilBertTokenizerFast  with return_tensors='tf' parameter in tokenizer because building model in a tensorflow framework

Model: TFDistilBertForSequenceClassification 
 
Optimizer: Adam with learning rate=5e-5

Loss: SparseCategoricalCrossentropy

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

After Tokenized, Encoded datasets are converted to Dataset Objects by using tf.data.Dataset.from_tensor_slices((dict(train_encoding),  train_y))

This step is done to inject a dataset into TFModel in a specific TF format

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Epoch |
|:----------:|:--------------:|:-----:|
| 0.0754     | 0.9803         | 0     |
| 0.0252     | 0.9935         | 1     |
| 0.0114     | 0.9962         | 2     |


### Framework versions

- Transformers 4.21.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Manirathinam21/DistilBert_SMSSpam_classifier", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Manirathinam21/DistilBert\_SMSSpam\_classifier
==============================================


This model is a fine-tuned version of distilbert-base-uncased on an SMSSpam Detection dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.0114
* Train Accuracy: 0.9962
* Epoch: 2


Target Labels
-------------


label: a classification label, with possible values including


* Ham : 0
* Spam : 1


Model description
-----------------


Tokenizer used is DistilBertTokenizerFast with return\_tensors='tf' parameter in tokenizer because building model in a tensorflow framework


Model: TFDistilBertForSequenceClassification


Optimizer: Adam with learning rate=5e-5


Loss: SparseCategoricalCrossentropy


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


After Tokenized, Encoded datasets are converted to Dataset Objects by using URL.Dataset.from\_tensor\_slices((dict(train\_encoding), train\_y))


This step is done to inject a dataset into TFModel in a specific TF format


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.21.1
* TensorFlow 2.8.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.21.1
* TensorFlow 2.8.2
* Tokenizers 0.12.1
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.21.1
* TensorFlow 2.8.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.21.1
* TensorFlow 2.8.2
* Tokenizers 0.12.1