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# Question Answering with Hugging Face Transformers and Keras 🤗❤️

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on SQuAD dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9300
- Validation Loss: 1.1437
- Epoch: 1

## Model description

Question answering model based on distilbert-base-cased, trained with 🤗Transformers + ❤️Keras.

## Intended uses & limitations

This model is trained for Question Answering tutorial for Keras.io.

## Training and evaluation data

It is trained on [SQuAD](https://huggingface.co/datasets/squad) question answering dataset. ⁉️ 

## Training procedure

Find the notebook in Keras Examples [here](https://keras.io/examples/nlp/question_answering/). ❤️

### 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: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5145     | 1.1500          | 0     |
| 0.9300     | 1.1437          | 1     |
 

### Framework versions

- Transformers 4.16.0.dev0
- TensorFlow 2.6.0
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["squad"], "metrics": ["f1"], "widget": [{"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."}], "base_model": "distilbert-base-cased", "model-index": [{"name": "transformers-qa", "results": []}]}
transformers tf distilbert question-answering generated_from_keras_callback dataset:squad base_model:distilbert-base-cased license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #dataset-squad #base_model-distilbert-base-cased #license-apache-2.0 #endpoints_compatible #has_space #region-us 

Question Answering with Hugging Face Transformers and Keras ️
=============================================================


This model is a fine-tuned version of distilbert-base-cased on SQuAD dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.9300
* Validation Loss: 1.1437
* Epoch: 1


Model description
-----------------


Question answering model based on distilbert-base-cased, trained with Transformers + ️Keras.


Intended uses & limitations
---------------------------


This model is trained for Question Answering tutorial for URL.


Training and evaluation data
----------------------------


It is trained on SQuAD question answering dataset. ⁉️


Training procedure
------------------


Find the notebook in Keras Examples here. ️


### 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: mixed\_float16


### Training results



### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.6.0
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3



### 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: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.6.0
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3
TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #dataset-squad #base_model-distilbert-base-cased #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### 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: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.6.0
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3
TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #dataset-squad #base_model-distilbert-base-cased #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### 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: mixed\_float16### Training results### Framework versions


* Transformers 4.16.0.dev0
* TensorFlow 2.6.0
* Datasets 1.16.2.dev0
* Tokenizers 0.10.3