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# roberta-finetuned-qa-policy

This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the [Policy_QA](https://huggingface.co/datasets/GIZ/policy_qa_v0) dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

[Policy QA](https://huggingface.co/datasets/GIZ/policy_qa_v0)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["GIZ/policy_qa_v0"], "model-index": [{"name": "roberta-finetuned-qa-policy", "results": []}]}
transformers pytorch tensorboard roberta question-answering generated_from_trainer dataset:GIZ/policy_qa_v0 license:cc-by-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-GIZ/policy_qa_v0 #license-cc-by-4.0 #endpoints_compatible #region-us 




# roberta-finetuned-qa-policy

This model is a fine-tuned version of deepset/roberta-base-squad2 on the Policy_QA dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Policy QA

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
# roberta-finetuned-qa-policy

This model is a fine-tuned version of deepset/roberta-base-squad2 on the Policy_QA dataset. ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

Policy QA ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-GIZ/policy_qa_v0 #license-cc-by-4.0 #endpoints_compatible #region-us 
 # roberta-finetuned-qa-policy

This model is a fine-tuned version of deepset/roberta-base-squad2 on the Policy_QA dataset. ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

Policy QA ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-GIZ/policy_qa_v0 #license-cc-by-4.0 #endpoints_compatible #region-us 
# roberta-finetuned-qa-policy

This model is a fine-tuned version of deepset/roberta-base-squad2 on the Policy_QA dataset.## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

Policy QA## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2