
# Model

t5-base-msmarco-squad-query-generation-longp-v2

Task: query generation 
Architecture: LongT5

Base model: google/long-t5-tglobal-base

Note: This is supposed to be a baseline model.


## Prompt:

"Generate Query: {document}. Query:"

## Sequence length:

1536 tokens

## Training details

### Hyperparameters

Batch size: 8;  
Gradient acc: 8;
LR: 3e-4, linear scheduler, 400 warmup steps.  


### Data

Total: 252059 pairs (document, query) 

From MARCO-V2: 165238
From SQuAD: 86821

The remaining queries from MARCO-V2 train split were not used.

## Evaluation

This model is supposed to be used for data augmentation.
Hence, meaningful evaluation will come from downstream tasks.

MARCO-V2 Dev1:
BLEU: 0.102
ROUGE: 0.447

MARCO-V2 Dev2:
BLEU: 0.1691
ROUGE: 0.5013
{"language": ["en"], "license": "wtfpl", "datasets": ["ms_marco", "squad"]}
transformers pytorch longt5 text2text-generation en dataset:ms_marco dataset:squad license:wtfpl autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #longt5 #text2text-generation #en #dataset-ms_marco #dataset-squad #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us 


# Model

t5-base-msmarco-squad-query-generation-longp-v2

Task: query generation 
Architecture: LongT5

Base model: google/long-t5-tglobal-base

Note: This is supposed to be a baseline model.


## Prompt:

"Generate Query: {document}. Query:"

## Sequence length:

1536 tokens

## Training details

### Hyperparameters

Batch size: 8;  
Gradient acc: 8;
LR: 3e-4, linear scheduler, 400 warmup steps.  


### Data

Total: 252059 pairs (document, query) 

From MARCO-V2: 165238
From SQuAD: 86821

The remaining queries from MARCO-V2 train split were not used.

## Evaluation

This model is supposed to be used for data augmentation.
Hence, meaningful evaluation will come from downstream tasks.

MARCO-V2 Dev1:
BLEU: 0.102
ROUGE: 0.447

MARCO-V2 Dev2:
BLEU: 0.1691
ROUGE: 0.5013
# Model

t5-base-msmarco-squad-query-generation-longp-v2

Task: query generation 
Architecture: LongT5

Base model: google/long-t5-tglobal-base

Note: This is supposed to be a baseline model. ## Prompt:

"Generate Query: {document}. Query:" ## Sequence length:

1536 tokens ## Training details ### Hyperparameters

Batch size: 8;  
Gradient acc: 8;
LR: 3e-4, linear scheduler, 400 warmup steps. ### Data

Total: 252059 pairs (document, query) 

From MARCO-V2: 165238
From SQuAD: 86821

The remaining queries from MARCO-V2 train split were not used. ## Evaluation

This model is supposed to be used for data augmentation.
Hence, meaningful evaluation will come from downstream tasks.

MARCO-V2 Dev1:
BLEU: 0.102
ROUGE: 0.447

MARCO-V2 Dev2:
BLEU: 0.1691
ROUGE: 0.5013
TAGS
#transformers #pytorch #longt5 #text2text-generation #en #dataset-ms_marco #dataset-squad #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us 
 # Model

t5-base-msmarco-squad-query-generation-longp-v2

Task: query generation 
Architecture: LongT5

Base model: google/long-t5-tglobal-base

Note: This is supposed to be a baseline model. ## Prompt:

"Generate Query: {document}. Query:" ## Sequence length:

1536 tokens ## Training details ### Hyperparameters

Batch size: 8;  
Gradient acc: 8;
LR: 3e-4, linear scheduler, 400 warmup steps. ### Data

Total: 252059 pairs (document, query) 

From MARCO-V2: 165238
From SQuAD: 86821

The remaining queries from MARCO-V2 train split were not used. ## Evaluation

This model is supposed to be used for data augmentation.
Hence, meaningful evaluation will come from downstream tasks.

MARCO-V2 Dev1:
BLEU: 0.102
ROUGE: 0.447

MARCO-V2 Dev2:
BLEU: 0.1691
ROUGE: 0.5013
TAGS
#transformers #pytorch #longt5 #text2text-generation #en #dataset-ms_marco #dataset-squad #license-wtfpl #autotrain_compatible #endpoints_compatible #region-us 
# Model

t5-base-msmarco-squad-query-generation-longp-v2

Task: query generation 
Architecture: LongT5

Base model: google/long-t5-tglobal-base

Note: This is supposed to be a baseline model.## Prompt:

"Generate Query: {document}. Query:"## Sequence length:

1536 tokens## Training details### Hyperparameters

Batch size: 8;  
Gradient acc: 8;
LR: 3e-4, linear scheduler, 400 warmup steps.### Data

Total: 252059 pairs (document, query) 

From MARCO-V2: 165238
From SQuAD: 86821

The remaining queries from MARCO-V2 train split were not used.## Evaluation

This model is supposed to be used for data augmentation.
Hence, meaningful evaluation will come from downstream tasks.

MARCO-V2 Dev1:
BLEU: 0.102
ROUGE: 0.447

MARCO-V2 Dev2:
BLEU: 0.1691
ROUGE: 0.5013