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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: Chakshu/conversation_terminator_classifier |
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results: [] |
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datasets: |
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- Chakshu/conversation_ender |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# Chakshu/conversation_terminator_classifier |
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0364 |
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- Train Binary Accuracy: 0.9915 |
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- Epoch: 8 |
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## Example Usage |
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```py |
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from transformers import AutoTokenizer, TFBertForSequenceClassification, BertTokenizer |
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import tensorflow as tf |
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model_name = 'Chakshu/conversation_terminator_classifier' |
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tokenizer = BertTokenizer.from_pretrained(model_name) |
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model = TFBertForSequenceClassification.from_pretrained(model_name) |
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inputs = tokenizer("I will talk to you later", return_tensors="np", padding=True) |
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outputs = model(inputs.input_ids, inputs.attention_mask) |
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probabilities = tf.nn.sigmoid(outputs.logits) |
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# Round the probabilities to the nearest integer to get the class prediction |
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predicted_class = tf.round(probabilities) |
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print("The last message by the user indicates that the conversation has", "'ENDED'" if int(predicted_class.numpy()) == 1 else "'NOT ENDED'") |
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``` |
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## Model description |
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Classifies if the user is ending the conversation or wanting to continue it. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train Binary Accuracy | Epoch | |
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|:----------:|:---------------------:|:-----:| |
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| 0.2552 | 0.9444 | 0 | |
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| 0.1295 | 0.9872 | 1 | |
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| 0.0707 | 0.9872 | 2 | |
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| 0.0859 | 0.9829 | 3 | |
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| 0.0484 | 0.9872 | 4 | |
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| 0.0363 | 0.9957 | 5 | |
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| 0.0209 | 1.0 | 6 | |
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| 0.0268 | 0.9957 | 7 | |
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| 0.0364 | 0.9915 | 8 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- TensorFlow 2.12.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
