| --- |
| license: apache-2.0 |
| base_model: google/flan-t5-base |
| tags: |
| - generated_from_keras_callback |
| model-index: |
| - name: kaytoo2022/t5_technical_qa_with_react |
| results: [] |
| inference: true |
| library_name: transformers |
| pipeline_tag: text2text-generation |
| widget: |
| - text: >- |
| summarize: function Example() { |
| let [isLoading, setIsLoading] = React.useState(false); |
| |
| let handlePress = () => { |
| // Trigger button pending state |
| setIsLoading(true); |
| |
| setTimeout(() => { |
| // Cancel button pending state |
| setIsLoading(false); |
| }, 3000); |
| }; |
| |
| return ( |
| <Button variant="primary" isPending={isLoading} onPress={handlePress}> |
| Click me! |
| </Button> |
| ); |
| } |
| example_title: Question answering |
| - text: >- |
| question: What does the setTimeout function do? context: function Example() { |
| let [isLoading, setIsLoading] = React.useState(false); |
| |
| let handlePress = () => { |
| // Trigger button pending state |
| setIsLoading(true); |
| |
| setTimeout(() => { |
| // Cancel button pending state |
| setIsLoading(false); |
| }, 3000); |
| }; |
| |
| return ( |
| <Button variant="primary" isPending={isLoading} onPress={handlePress}> |
| Click me! |
| </Button> |
| ); |
| } |
| example_title: Summarization |
|
|
| --- |
| |
| <!-- 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. --> |
|
|
| # kaytoo2022/t5_technical_qa_with_react |
|
|
| This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Train Loss: 2.0191 |
| - Validation Loss: 2.0546 |
| - Epoch: 3 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} |
| - training_precision: float32 |
|
|
| ### Training results |
|
|
| | Train Loss | Validation Loss | Epoch | |
| |:----------:|:---------------:|:-----:| |
| | 2.5717 | 2.2548 | 0 | |
| | 2.2680 | 2.1607 | 1 | |
| | 2.1248 | 2.1008 | 2 | |
| | 2.0191 | 2.0546 | 3 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.42.4 |
| - TensorFlow 2.17.0 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
