| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_keras_callback |
| model-index: |
| - name: Chakshu/conversation_terminator_classifier |
| results: [] |
| datasets: |
| - Chakshu/conversation_ender |
| language: |
| - en |
| --- |
| |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should |
| probably proofread and complete it, then remove this comment. --> |
|
|
| # 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 |
