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# Chakshu/conversation_terminator_classifier

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0364
- Train Binary Accuracy: 0.9915
- Epoch: 8

## Example Usage
```py
from transformers import AutoTokenizer, TFBertForSequenceClassification, BertTokenizer
import tensorflow as tf

model_name = 'Chakshu/conversation_terminator_classifier' 

tokenizer = BertTokenizer.from_pretrained(model_name)
model = TFBertForSequenceClassification.from_pretrained(model_name)
inputs = tokenizer("I will talk to you later", return_tensors="np", padding=True)
outputs = model(inputs.input_ids, inputs.attention_mask)
probabilities = tf.nn.sigmoid(outputs.logits)

# Round the probabilities to the nearest integer to get the class prediction
predicted_class = tf.round(probabilities)
print("The last message by the user indicates that the conversation has", "'ENDED'" if int(predicted_class.numpy()) == 1 else "'NOT ENDED'")
```

## Model description

Classifies if the user is ending the conversation or wanting to continue it.

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Binary Accuracy | Epoch |
|:----------:|:---------------------:|:-----:|
| 0.2552     | 0.9444                | 0     |
| 0.1295     | 0.9872                | 1     |
| 0.0707     | 0.9872                | 2     |
| 0.0859     | 0.9829                | 3     |
| 0.0484     | 0.9872                | 4     |
| 0.0363     | 0.9957                | 5     |
| 0.0209     | 1.0                   | 6     |
| 0.0268     | 0.9957                | 7     |
| 0.0364     | 0.9915                | 8     |


### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["Chakshu/conversation_ender"], "model-index": [{"name": "Chakshu/conversation_terminator_classifier", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback en dataset:Chakshu/conversation_ender license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #en #dataset-Chakshu/conversation_ender #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Chakshu/conversation\_terminator\_classifier
============================================


This model is a fine-tuned version of google/mobilebert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.0364
* Train Binary Accuracy: 0.9915
* Epoch: 8


Example Usage
-------------


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


Classifies if the user is ending the conversation or wanting to continue it.


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


More information needed


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


More information needed


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


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #en #dataset-Chakshu/conversation_ender #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3