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# rob-base-gc1

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.

## 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:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "quoref", "adversarial_qa", "duorc"], "model-index": [{"name": "rob-base-gc1", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "adversarial_qa", "type": "adversarial_qa", "config": "adversarialQA", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 42.9, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 53.8954, "name": "F1", "verified": true}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 79.5382, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 82.7221, "name": "F1", "verified": true}]}, {"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "quoref", "type": "quoref", "config": "default", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 78.403, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 82.1408, "name": "F1", "verified": true}]}]}]}
transformers pytorch optimum_graphcore roberta question-answering generated_from_trainer dataset:squad_v2 dataset:quoref dataset:adversarial_qa dataset:duorc license:mit model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #optimum_graphcore #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us 




# rob-base-gc1

This model is a fine-tuned version of roberta-base on the None dataset.

## 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:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1

# rob-base-gc1

This model is a fine-tuned version of roberta-base on the None dataset. ## 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:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #optimum_graphcore #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us 
 # rob-base-gc1

This model is a fine-tuned version of roberta-base on the None dataset. ## 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:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #optimum_graphcore #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #dataset-quoref #dataset-adversarial_qa #dataset-duorc #license-mit #model-index #endpoints_compatible #region-us 
# rob-base-gc1

This model is a fine-tuned version of roberta-base on the None dataset.## 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:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision### Training results### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1