# Dataset Card for "Lawyer-Instruct"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)

## Dataset Description

### Dataset Summary

Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training.

Dataset generated in part by dang/futures 
### Supported Tasks and Leaderboards

- `dialogue-modeling`: The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses.
- There is no official leaderboard associated with this dataset at this time.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example:

```json
{
    "instruction": "What are the possible legal consequences of not paying taxes?",
    "input": "",
    "output": "There can be several legal consequences, ranging from fines to imprisonment..."
}
```

### Data Fields

- `instruction`: a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training.
- `input`: 
- `output`: a string representing the legal professional's response.

### Data Splits

This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.

license:apache-2.0 region:us
{"license": "apache-2.0"}
TAGS
#license-apache-2.0 #region-us 

# Dataset Card for "Lawyer-Instruct"

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits

## Dataset Description

### Dataset Summary

Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training.

Dataset generated in part by dang/futures 
### Supported Tasks and Leaderboards

- 'dialogue-modeling': The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses.
- There is no official leaderboard associated with this dataset at this time.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example:



### Data Fields

- 'instruction': a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training.
- 'input': 
- 'output': a string representing the legal professional's response.

### Data Splits

This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.

# Dataset Card for "Lawyer-Instruct" ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits ## Dataset Description ### Dataset Summary

Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training.

Dataset generated in part by dang/futures ### Supported Tasks and Leaderboards

- 'dialogue-modeling': The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses.
- There is no official leaderboard associated with this dataset at this time. ### Languages

The text in the dataset is in English. ## Dataset Structure ### Data Instances

An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example: ### Data Fields

- 'instruction': a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training.
- 'input': 
- 'output': a string representing the legal professional's response. ### Data Splits

This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.
TAGS
#license-apache-2.0 #region-us 
 # Dataset Card for "Lawyer-Instruct" ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits ## Dataset Description ### Dataset Summary

Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training.

Dataset generated in part by dang/futures ### Supported Tasks and Leaderboards

- 'dialogue-modeling': The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses.
- There is no official leaderboard associated with this dataset at this time. ### Languages

The text in the dataset is in English. ## Dataset Structure ### Data Instances

An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example: ### Data Fields

- 'instruction': a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training.
- 'input': 
- 'output': a string representing the legal professional's response. ### Data Splits

This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.
passage: TAGS
#license-apache-2.0 #region-us 
# Dataset Card for "Lawyer-Instruct"## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits## Dataset Description### Dataset Summary

Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training.

Dataset generated in part by dang/futures### Supported Tasks and Leaderboards

- 'dialogue-modeling': The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses.
- There is no official leaderboard associated with this dataset at this time.### Languages

The text in the dataset is in English.## Dataset Structure### Data Instances

An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example:### Data Fields

- 'instruction': a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training.
- 'input': 
- 'output': a string representing the legal professional's response.### Data Splits

This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.