
# Dataset Card for Squad_Chat

## Dataset Description

A data set for training LLMs on in-context or Document Question-Answering.

- Point of Contact: info@alderley.ai 

### Dataset Summary

This dataset is an amended version of the SQuAD2.0 dataset, with the question responses amended to be more conversational in nature. 

The SQuAD2.0 dataset combines the original set of 100,000 questions from SQuAD1.1 with an additional 50,000 unanswerable questions, crafted intentionally by crowdworkers to mimic the format and appearance of the answerable ones. This approach requires systems to not only deliver accurate answers when they exist, but also determine when the provided paragraph does not support an answer and accordingly refrain from responding.

Squad_Chat is unique in that the question responses are in a more conversational chat format. The objective of this transformation is to support the fine-tune training of large language models so that they perform well specifically with in context question-answer tasks.

### Supported Tasks and Leaderboards

Supported Task: In Context Question-Answer, Document Question-Answer tasks 

## Dataset Structure

We provide both csv and jsonl files. 
Questions that are unaswerable are NaNs in the csv, and have the string `"<no answer>"` in the jsonl 

### Data Fields

The csv dataset has the following attributes (all strings):
id: Matches the original squad id
title: Matches the original squad title
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

The jsonl file only has:
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

### Data Splits

None, data is presented as a single file.
Note that in the original squad dataset, the dataset is split into Train and Validation sets. In Squad_Chat, both train data and validation data are combined into a single file. 

## Dataset Creation

26th June 2023

### Curation Rationale

This data set is specifically to support the training of large language models for in-context question-answering or document question-answering. Small Instruct and chat trained LLMs struggle with this task and have a tendency to ignore the provided context when generating an output. This data set is designed to  support the training of small LLMs that excel at this task.

### Source Data

Squad2
https://huggingface.co/datasets/squad_v2

#### Initial Data Collection and Normalization

This new answer data set was generated from the original squad_v2 data set over several days by querying gpt-3.5turbo with the following prompt...

```
system_intel = """For each of the 60 input data items, rewrite the given answer in a more conversational tone. Do not add additional information, just rephrase it. The input data items consist of an ID, a question, and an answer. Your task is to return a valid JSON object for each item, containing the original ID and your rephrased answer. Remember, the keys "id" and "answer" in your JSON object should be in double quotes (""). If any quotations appear in the answer, use single quotes (''). 

For instance:
- If the input is: [0, 'In what country is Normandy located?', ' France'], the output should be: {"id" : 0, "answer": "Normandy is located in the country of France."}.
- If the input is: [2265, 'What is the Rankine cycle sometimes called?',  'a practical Carnot cycle'], the output should be: {"id" :2265, "answer":  "The Rankine cycle is also sometimes known as a practical Carnot cycle."}.
- if the input is: [9524 \'What campaign did the Scottish National Party (SNP) run?\'\n  \'The SNP ran with the campaign "Its Scotlands Oil".\'], the output should be : {"id": 9524, "answer": "The Scottish National Party (SNP) ran with the campaign \'Its Scotlands Oil\'."}
"""
prompt = f"Here is the list of 60 data items: {item_list}"

```

## Considerations for Using the Data

### Discussion of Biases

The data is only in English.
There is a 2/3 : 1/3 split of answered to not answered questions.


### Contributions

Alderley.ai
task_categories:question-answering size_categories:100K<n<1M language:en license:cc-by-sa-4.0 region:us
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering"]}
TAGS
#task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc-by-sa-4.0 #region-us 


# Dataset Card for Squad_Chat

## Dataset Description

A data set for training LLMs on in-context or Document Question-Answering.

- Point of Contact: info@URL 

### Dataset Summary

This dataset is an amended version of the SQuAD2.0 dataset, with the question responses amended to be more conversational in nature. 

The SQuAD2.0 dataset combines the original set of 100,000 questions from SQuAD1.1 with an additional 50,000 unanswerable questions, crafted intentionally by crowdworkers to mimic the format and appearance of the answerable ones. This approach requires systems to not only deliver accurate answers when they exist, but also determine when the provided paragraph does not support an answer and accordingly refrain from responding.

Squad_Chat is unique in that the question responses are in a more conversational chat format. The objective of this transformation is to support the fine-tune training of large language models so that they perform well specifically with in context question-answer tasks.

### Supported Tasks and Leaderboards

Supported Task: In Context Question-Answer, Document Question-Answer tasks 

## Dataset Structure

We provide both csv and jsonl files. 
Questions that are unaswerable are NaNs in the csv, and have the string '"<no answer>"' in the jsonl 

### Data Fields

The csv dataset has the following attributes (all strings):
id: Matches the original squad id
title: Matches the original squad title
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

The jsonl file only has:
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

### Data Splits

None, data is presented as a single file.
Note that in the original squad dataset, the dataset is split into Train and Validation sets. In Squad_Chat, both train data and validation data are combined into a single file. 

## Dataset Creation

26th June 2023

### Curation Rationale

This data set is specifically to support the training of large language models for in-context question-answering or document question-answering. Small Instruct and chat trained LLMs struggle with this task and have a tendency to ignore the provided context when generating an output. This data set is designed to  support the training of small LLMs that excel at this task.

### Source Data

Squad2
URL

#### Initial Data Collection and Normalization

This new answer data set was generated from the original squad_v2 data set over several days by querying gpt-3.5turbo with the following prompt...



## Considerations for Using the Data

### Discussion of Biases

The data is only in English.
There is a 2/3 : 1/3 split of answered to not answered questions.


### Contributions

URL
# Dataset Card for Squad_Chat ## Dataset Description

A data set for training LLMs on in-context or Document Question-Answering.

- Point of Contact: info@URL ### Dataset Summary

This dataset is an amended version of the SQuAD2.0 dataset, with the question responses amended to be more conversational in nature. 

The SQuAD2.0 dataset combines the original set of 100,000 questions from SQuAD1.1 with an additional 50,000 unanswerable questions, crafted intentionally by crowdworkers to mimic the format and appearance of the answerable ones. This approach requires systems to not only deliver accurate answers when they exist, but also determine when the provided paragraph does not support an answer and accordingly refrain from responding.

Squad_Chat is unique in that the question responses are in a more conversational chat format. The objective of this transformation is to support the fine-tune training of large language models so that they perform well specifically with in context question-answer tasks. ### Supported Tasks and Leaderboards

Supported Task: In Context Question-Answer, Document Question-Answer tasks ## Dataset Structure

We provide both csv and jsonl files. 
Questions that are unaswerable are NaNs in the csv, and have the string '"<no answer>"' in the jsonl ### Data Fields

The csv dataset has the following attributes (all strings):
id: Matches the original squad id
title: Matches the original squad title
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

The jsonl file only has:
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer) ### Data Splits

None, data is presented as a single file.
Note that in the original squad dataset, the dataset is split into Train and Validation sets. In Squad_Chat, both train data and validation data are combined into a single file. ## Dataset Creation

26th June 2023 ### Curation Rationale

This data set is specifically to support the training of large language models for in-context question-answering or document question-answering. Small Instruct and chat trained LLMs struggle with this task and have a tendency to ignore the provided context when generating an output. This data set is designed to  support the training of small LLMs that excel at this task. ### Source Data

Squad2
URL #### Initial Data Collection and Normalization

This new answer data set was generated from the original squad_v2 data set over several days by querying gpt-3.5turbo with the following prompt... ## Considerations for Using the Data ### Discussion of Biases

The data is only in English.
There is a 2/3 : 1/3 split of answered to not answered questions. ### Contributions

URL
TAGS
#task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc-by-sa-4.0 #region-us 
 # Dataset Card for Squad_Chat ## Dataset Description

A data set for training LLMs on in-context or Document Question-Answering.

- Point of Contact: info@URL ### Dataset Summary

This dataset is an amended version of the SQuAD2.0 dataset, with the question responses amended to be more conversational in nature. 

The SQuAD2.0 dataset combines the original set of 100,000 questions from SQuAD1.1 with an additional 50,000 unanswerable questions, crafted intentionally by crowdworkers to mimic the format and appearance of the answerable ones. This approach requires systems to not only deliver accurate answers when they exist, but also determine when the provided paragraph does not support an answer and accordingly refrain from responding.

Squad_Chat is unique in that the question responses are in a more conversational chat format. The objective of this transformation is to support the fine-tune training of large language models so that they perform well specifically with in context question-answer tasks. ### Supported Tasks and Leaderboards

Supported Task: In Context Question-Answer, Document Question-Answer tasks ## Dataset Structure

We provide both csv and jsonl files. 
Questions that are unaswerable are NaNs in the csv, and have the string '"<no answer>"' in the jsonl ### Data Fields

The csv dataset has the following attributes (all strings):
id: Matches the original squad id
title: Matches the original squad title
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

The jsonl file only has:
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer) ### Data Splits

None, data is presented as a single file.
Note that in the original squad dataset, the dataset is split into Train and Validation sets. In Squad_Chat, both train data and validation data are combined into a single file. ## Dataset Creation

26th June 2023 ### Curation Rationale

This data set is specifically to support the training of large language models for in-context question-answering or document question-answering. Small Instruct and chat trained LLMs struggle with this task and have a tendency to ignore the provided context when generating an output. This data set is designed to  support the training of small LLMs that excel at this task. ### Source Data

Squad2
URL #### Initial Data Collection and Normalization

This new answer data set was generated from the original squad_v2 data set over several days by querying gpt-3.5turbo with the following prompt... ## Considerations for Using the Data ### Discussion of Biases

The data is only in English.
There is a 2/3 : 1/3 split of answered to not answered questions. ### Contributions

URL
passage: TAGS
#task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc-by-sa-4.0 #region-us 
# Dataset Card for Squad_Chat## Dataset Description

A data set for training LLMs on in-context or Document Question-Answering.

- Point of Contact: info@URL### Dataset Summary

This dataset is an amended version of the SQuAD2.0 dataset, with the question responses amended to be more conversational in nature. 

The SQuAD2.0 dataset combines the original set of 100,000 questions from SQuAD1.1 with an additional 50,000 unanswerable questions, crafted intentionally by crowdworkers to mimic the format and appearance of the answerable ones. This approach requires systems to not only deliver accurate answers when they exist, but also determine when the provided paragraph does not support an answer and accordingly refrain from responding.

Squad_Chat is unique in that the question responses are in a more conversational chat format. The objective of this transformation is to support the fine-tune training of large language models so that they perform well specifically with in context question-answer tasks.### Supported Tasks and Leaderboards

Supported Task: In Context Question-Answer, Document Question-Answer tasks## Dataset Structure

We provide both csv and jsonl files. 
Questions that are unaswerable are NaNs in the csv, and have the string '"<no answer>"' in the jsonl### Data Fields

The csv dataset has the following attributes (all strings):
id: Matches the original squad id
title: Matches the original squad title
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)

The jsonl file only has:
context: Matches the original squad context
question: Matches the original question
answer: Conversational answer to question. (evolution of original squad answer)