
# t5-large-coqr-canard

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the [CANARD](https://sites.google.com/view/qanta/projects/canard) dataset.
It achieves the following results on the test set:
- Loss: 0.3064
- Bleu: 77.1979
- Generation Length: 9.576

## Model description

CANARD dataset rewrites the original questions in conversations to make them context-independent (understandable w/o context).
On the contrary, this model is trained to rewrite context-independent questions to conversational questions, aiming to create fluent dialog with anaphora and ellipsis.

Input: 
```
Rewrite the question according to the given context to make the dialog fluent using anaphora and ellipsis.

question: How did people respond to Superstar Billy Graham's return?

context: Superstar Billy Graham
Return to WWWF (1977-1981)
Why did he return to the WWWF?
an agreement with promoter Vincent J. McMahon (Senior
What was his agreement with McMahon?
I don't know.
```

Target:
```
How did people respond to his return?
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 512
- total_eval_batch_size: 512
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log        | 1.0   | 62   | 0.2987          | 77.2361 | 9.4534  |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1

{"language": ["en"], "license": "apache-2.0", "tags": ["t5-large", "text2text-generation", "conversational question rewriting"], "datasets": ["CANARD"], "metrics": ["BLEU"], "widget": [{"text": "Rewrite the question according to the given context to make the dialog fluent using anaphora and ellipsis.\n\nquestion: What else happened during 1977-1981 other than Superstar Billy Graham's return?\n\ncontext: Superstar Billy Graham\nReturn to WWWF (1977-1981)\nWhy did he return to the WWWF?\nan agreement with promoter Vincent J. McMahon (Senior\nWhat was his agreement with McMahon?\nI don't know.\nHow did people respond to his return?\nI don't know."}, {"text": "Rewrite the question according to the given context to make the dialog fluent using anaphora and ellipsis.\n\nquestion: why did Billy Graham personally sued Zahorian and the WWF?\n\ncontext: Superstar Billy Graham\nDisputes with the McMahons\nwhat disputes did he have?\nGraham personally sued Zahorian and the WWF,"}], "inference": {"parameters": {"max_length": 100}}, "model-index": [{"name": "t5-large-coqr-canard", "results": [{"task": {"type": "text2text-generation", "name": "conversational question rewriting"}, "dataset": {"name": "CANARD", "type": "CANARD", "split": "test"}, "metrics": [{"type": "BLEU", "value": 77.8, "name": "BLEU"}]}]}]}
transformers pytorch t5 text2text-generation t5-large conversational question rewriting en dataset:CANARD license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #t5-large #conversational question rewriting #en #dataset-CANARD #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-large-coqr-canard
====================


This model is a fine-tuned version of t5-large on the CANARD dataset.
It achieves the following results on the test set:


* Loss: 0.3064
* Bleu: 77.1979
* Generation Length: 9.576


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


CANARD dataset rewrites the original questions in conversations to make them context-independent (understandable w/o context).
On the contrary, this model is trained to rewrite context-independent questions to conversational questions, aiming to create fluent dialog with anaphora and ellipsis.


Input:


Target:


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 512
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 512
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #t5 #text2text-generation #t5-large #conversational question rewriting #en #dataset-CANARD #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 512
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #t5 #text2text-generation #t5-large #conversational question rewriting #en #dataset-CANARD #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 512
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1