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--- |
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datasets: |
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- squad |
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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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metrics: |
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- f1 |
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model-index: |
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- name: transformers-qa |
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results: |
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- task: |
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name: "Question Answering" |
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type: question-answering |
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dataset: |
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type: squad |
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name: SQuAD |
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args: en |
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metrics: |
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[] |
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widget: |
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- context: "Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error." |
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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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# Question Answering with Hugging Face Transformers and Keras 🤗❤️ |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on SQuAD dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.9300 |
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- Validation Loss: 1.1437 |
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- Epoch: 1 |
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## Model description |
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Question answering model based on distilbert-base-cased, trained with 🤗Transformers + ❤️Keras. |
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## Intended uses & limitations |
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This model is trained for Question Answering tutorial for Keras.io. |
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## Training and evaluation data |
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It is trained on [SQuAD](https://huggingface.co/datasets/squad) question answering dataset. ⁉️ |
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## Training procedure |
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Find the notebook in Keras Examples [here](https://keras.io/examples/nlp/question_answering/). ❤️ |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: mixed_float16 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 1.5145 | 1.1500 | 0 | |
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| 0.9300 | 1.1437 | 1 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- TensorFlow 2.6.0 |
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- Datasets 1.16.2.dev0 |
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- Tokenizers 0.10.3 |
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