MIT-Restaurant

Overview

The MIT-Restaurant dataset is a collection of restaurant review text specifically curated for training and testing NLP models for Named Entity Recognition. It contains sentences from real reviews with annotations in BIO format.

Task Description

Task Type: Restaurant Domain Named Entity Recognition (NER)
Input: Restaurant review text and queries
Output: Identified restaurant-related entity spans
Domain: Food service, restaurant reviews, dialogue systems

Key Features

Real restaurant review sentences
BIO format annotations
Eight restaurant-specific entity types
Useful for food service domain NLP
Adapted for conversational AI applications

Evaluation Notes

Default configuration uses 5-shot evaluation
Metrics: Precision, Recall, F1-Score, Accuracy
Entity types: AMENITY, CUISINE, DISH, HOURS, LOCATION, PRICE, RATING, RESTAURANT_NAME

Properties

Property	Value
Benchmark Name	`mit_restaurant`
Dataset ID	extraordinarylab/mit-restaurant
Paper	N/A
Tags	`Knowledge`, `NER`
Metrics	`precision`, `recall`, `f1_score`, `accuracy`
Default Shots	5-shot
Evaluation Split	`test`
Train Split	`train`

Data Statistics

Metric	Value
Total Samples	1,521
Prompt Length (Mean)	2383.97 chars
Prompt Length (Min/Max)	2338 / 2474 chars

Sample Example

Subset: default

{
  "input": [
    {
      "id": "9d5a77f1",
      "content": "Here are some examples of named entity recognition:\n\nInput:\ncan you find me the cheapest mexican restaurant nearby\n\nOutput:\n<response>can you find me the <price>cheapest</price> <cuisine>mexican</cuisine> restaurant <location>nearby</location ... [TRUNCATED] ... mes provided.\n5. Do not include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\na four star restaurant with a bar\n"
    }
  ],
  "target": "<response>a <rating>four star</rating> restaurant <location>with a</location> <amenity>bar</amenity></response>",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "tokens": [
      "a",
      "four",
      "star",
      "restaurant",
      "with",
      "a",
      "bar"
    ],
    "ner_tags": [
      "O",
      "B-RATING",
      "I-RATING",
      "O",
      "B-LOCATION",
      "I-LOCATION",
      "B-AMENITY"
    ]
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

You are a named entity recognition system that identifies the following entity types:
{entities}

Process the provided text and mark all named entities with XML-style tags.

For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.

Available entity tags: {entity_list}

INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.

Text to process:
{text}

Few-shot Template

Here are some examples of named entity recognition:

{fewshot}

You are a named entity recognition system that identifies the following entity types:
{entities}

Process the provided text and mark all named entities with XML-style tags.

For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.

Available entity tags: {entity_list}

INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.

Text to process:
{text}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets mit_restaurant \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['mit_restaurant'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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