
# xtremedistil-l6-h256-uncased for QA 

This is a [xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) model, fine-tuned using the [NaturalQuestionsShort](https://research.google/pubs/pub47761/) dataset from the [MRQA Shared Task 2019](https://github.com/mrqa/MRQA-Shared-Task-2019) repository.

## Overview
**Language model:** xtremedistil-l6-h256-uncased  
**Language:** English  
**Downstream-task:** Extractive QA  
**Training data:** NaturalQuestionsShort  
**Eval data:** NaturalQuestionsShort  
**Infrastructure**: Google Colaboratory GPU

## Hyperparameters

```
batch_size = 16
n_epochs = 2
base_LM_model = "xtremedistil-l6-h256-uncased"
max_seq_len = 512
learning_rate = 3e-5
optimizer = AdamW
weight_decay = 0.01
lr_schedule = Linear
warmup_steps = 0
``` 

## Performance
The model was evaluated on the on the [NaturalQuestionsShort](https://research.google/pubs/pub47761/) dev set from the [MRQA Shared Task 2019](https://github.com/mrqa/MRQA-Shared-Task-2019) repository.

```
"exact_match": 46.914926768463694,
"f1": 63.863619507647456,
```

## UKP Square
This model can also be found on [UKP Square](https://square.ukp-lab.de/qa). This website from the [UKP lab at the TU Darmstadt](https://www.informatik.tu-darmstadt.de/ukp/ukp_home/index.en.jsp) is a platform to compare and evaluate cloud-hosted QA models via explainability techniques and behavioral tests.

## Author & Background
This model was created by Janik and Ben during the [DL4NLP course](https://github.com/dl4nlp-tuda/deep-learning-for-nlp-lectures) by [Ivan Habernal](https://www.trusthlt.org/)

{"language": "en", "license": "mit", "tags": ["natural-questions-short", "question-answering"]}
transformers pytorch bert question-answering natural-questions-short en license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #natural-questions-short #en #license-mit #endpoints_compatible #region-us 


# xtremedistil-l6-h256-uncased for QA 

This is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 repository.

## Overview
Language model: xtremedistil-l6-h256-uncased  
Language: English  
Downstream-task: Extractive QA  
Training data: NaturalQuestionsShort  
Eval data: NaturalQuestionsShort  
Infrastructure: Google Colaboratory GPU

## Hyperparameters

 

## Performance
The model was evaluated on the on the NaturalQuestionsShort dev set from the MRQA Shared Task 2019 repository.



## UKP Square
This model can also be found on UKP Square. This website from the UKP lab at the TU Darmstadt is a platform to compare and evaluate cloud-hosted QA models via explainability techniques and behavioral tests.

## Author & Background
This model was created by Janik and Ben during the DL4NLP course by Ivan Habernal

# xtremedistil-l6-h256-uncased for QA 

This is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 repository. ## Overview
Language model: xtremedistil-l6-h256-uncased  
Language: English  
Downstream-task: Extractive QA  
Training data: NaturalQuestionsShort  
Eval data: NaturalQuestionsShort  
Infrastructure: Google Colaboratory GPU ## Hyperparameters ## Performance
The model was evaluated on the on the NaturalQuestionsShort dev set from the MRQA Shared Task 2019 repository. ## UKP Square
This model can also be found on UKP Square. This website from the UKP lab at the TU Darmstadt is a platform to compare and evaluate cloud-hosted QA models via explainability techniques and behavioral tests. ## Author & Background
This model was created by Janik and Ben during the DL4NLP course by Ivan Habernal
TAGS
#transformers #pytorch #bert #question-answering #natural-questions-short #en #license-mit #endpoints_compatible #region-us 
 # xtremedistil-l6-h256-uncased for QA 

This is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 repository. ## Overview
Language model: xtremedistil-l6-h256-uncased  
Language: English  
Downstream-task: Extractive QA  
Training data: NaturalQuestionsShort  
Eval data: NaturalQuestionsShort  
Infrastructure: Google Colaboratory GPU ## Hyperparameters ## Performance
The model was evaluated on the on the NaturalQuestionsShort dev set from the MRQA Shared Task 2019 repository. ## UKP Square
This model can also be found on UKP Square. This website from the UKP lab at the TU Darmstadt is a platform to compare and evaluate cloud-hosted QA models via explainability techniques and behavioral tests. ## Author & Background
This model was created by Janik and Ben during the DL4NLP course by Ivan Habernal
passage: TAGS
#transformers #pytorch #bert #question-answering #natural-questions-short #en #license-mit #endpoints_compatible #region-us 
# xtremedistil-l6-h256-uncased for QA 

This is a xtremedistil-l6-h256-uncased model, fine-tuned using the NaturalQuestionsShort dataset from the MRQA Shared Task 2019 repository.## Overview
Language model: xtremedistil-l6-h256-uncased  
Language: English  
Downstream-task: Extractive QA  
Training data: NaturalQuestionsShort  
Eval data: NaturalQuestionsShort  
Infrastructure: Google Colaboratory GPU## Hyperparameters## Performance
The model was evaluated on the on the NaturalQuestionsShort dev set from the MRQA Shared Task 2019 repository.## UKP Square
This model can also be found on UKP Square. This website from the UKP lab at the TU Darmstadt is a platform to compare and evaluate cloud-hosted QA models via explainability techniques and behavioral tests.## Author & Background
This model was created by Janik and Ben during the DL4NLP course by Ivan Habernal