license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Intent-Classification-Bert-Base-Cased
results: []
Intent-Classification-Bert-Base-Cased
This model is a fine-tuned version of bert-base-cased on an Intent-Classification-Commands dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.6110
- Train Sparse Categorical Accuracy: 0.9836
- Validation Loss: 0.4073
- Validation Sparse Categorical Accuracy: 0.9583
- Epoch: 3
Model description
Base model: 'bert-base-cased' can be used for intent classification. It trained on the Intent-Classification-Commands dataset. With the following classes-
{
"0": "asking date",
"1": "asking time",
"2": "asking weather",
"3": "check internet speed",
"4": "click photo",
"5": "covid cases",
"6": "download youtube video",
"7": "goodbye",
"8": "greet",
"9": "open website",
"10": "play games",
"11": "play on youtube",
"12": "send email",
"13": "send whatsapp message",
"14": "take screenshot",
"15": "tell me about",
"16": "tell me joke",
"17": "tell me news"
}
Intended uses & limitations
Intent Classifications for Chatbot or Virtual Assistant. Only supports the English language. It can't work in outside classes. But you can fine-tune it for your own use.
Training and evaluation data
Dataset Used: Intent-Classification-Commands
Training procedure
https://colab.research.google.com/drive/1KHg14glvhdV_ziOcY0pHm66PBYoBZMS0?usp=sharing
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 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
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