# Pathfinder-X2 

license: CC BY 4.0, Free to use for any purpose, including commercial, with attribution.

The Pathfinder and Pathfinder-X datasets have been crucial for training Large Language Models with Long-Range Dependencies.  
In January of 2023, Meta's Mega LLM scored a 97% on the Pathfinder-X dataset, indicating a need for an even more challenging benchmark.
Pathfinder-X2 contains 200,000 512x512 images along with 200,000 segmentation masks for those images.  Each image contains an assortment
of dashed-line "snakes" of varying length, and a model's task is to segment only the snake with a circle on one end.
Each image is meant to be fed in as a sequence,pixel-by-pixel, into a Large Language Model.

Explanation paper: https://www.overleaf.com/read/rpsmdnxbdfjt

Based on the Pathfinder dataset by Drew Linsley, Alekh K Ashok, Lakshmi N Govindarajan, Rex Liu, and Thomas Serre.

![Sample input](https://huggingface.co/datasets/Tylersuard/PathfinderX2/blob/main/sample_0.png)
![Sample label](https://huggingface.co/datasets/Tylersuard/PathfinderX2/blob/main/seg_sample_0.png)

---
annotations_creators:
- Tyler Suard
tags:
- language
- nlp
- llm
- long-range
size_categories:
- 100K<n<1M
---
region:us
{}
TAGS
#region-us 

# Pathfinder-X2 

license: CC BY 4.0, Free to use for any purpose, including commercial, with attribution.

The Pathfinder and Pathfinder-X datasets have been crucial for training Large Language Models with Long-Range Dependencies.  
In January of 2023, Meta's Mega LLM scored a 97% on the Pathfinder-X dataset, indicating a need for an even more challenging benchmark.
Pathfinder-X2 contains 200,000 512x512 images along with 200,000 segmentation masks for those images.  Each image contains an assortment
of dashed-line "snakes" of varying length, and a model's task is to segment only the snake with a circle on one end.
Each image is meant to be fed in as a sequence,pixel-by-pixel, into a Large Language Model.

Explanation paper: URL

Based on the Pathfinder dataset by Drew Linsley, Alekh K Ashok, Lakshmi N Govindarajan, Rex Liu, and Thomas Serre.

!Sample input
!Sample label

---
annotations_creators:
- Tyler Suard
tags:
- language
- nlp
- llm
- long-range
size_categories:
- 100K<n<1M
---
# Pathfinder-X2 

license: CC BY 4.0, Free to use for any purpose, including commercial, with attribution.

The Pathfinder and Pathfinder-X datasets have been crucial for training Large Language Models with Long-Range Dependencies.  
In January of 2023, Meta's Mega LLM scored a 97% on the Pathfinder-X dataset, indicating a need for an even more challenging benchmark.
Pathfinder-X2 contains 200,000 512x512 images along with 200,000 segmentation masks for those images.  Each image contains an assortment
of dashed-line "snakes" of varying length, and a model's task is to segment only the snake with a circle on one end.
Each image is meant to be fed in as a sequence,pixel-by-pixel, into a Large Language Model.

Explanation paper: URL

Based on the Pathfinder dataset by Drew Linsley, Alekh K Ashok, Lakshmi N Govindarajan, Rex Liu, and Thomas Serre.

!Sample input
!Sample label

---
annotations_creators:
- Tyler Suard
tags:
- language
- nlp
- llm
- long-range
size_categories:
- 100K<n<1M
---
TAGS
#region-us 
 # Pathfinder-X2 

license: CC BY 4.0, Free to use for any purpose, including commercial, with attribution.

The Pathfinder and Pathfinder-X datasets have been crucial for training Large Language Models with Long-Range Dependencies.  
In January of 2023, Meta's Mega LLM scored a 97% on the Pathfinder-X dataset, indicating a need for an even more challenging benchmark.
Pathfinder-X2 contains 200,000 512x512 images along with 200,000 segmentation masks for those images.  Each image contains an assortment
of dashed-line "snakes" of varying length, and a model's task is to segment only the snake with a circle on one end.
Each image is meant to be fed in as a sequence,pixel-by-pixel, into a Large Language Model.

Explanation paper: URL

Based on the Pathfinder dataset by Drew Linsley, Alekh K Ashok, Lakshmi N Govindarajan, Rex Liu, and Thomas Serre.

!Sample input
!Sample label

---
annotations_creators:
- Tyler Suard
tags:
- language
- nlp
- llm
- long-range
size_categories:
- 100K<n<1M
---
passage: TAGS
#region-us 
# Pathfinder-X2 

license: CC BY 4.0, Free to use for any purpose, including commercial, with attribution.

The Pathfinder and Pathfinder-X datasets have been crucial for training Large Language Models with Long-Range Dependencies.  
In January of 2023, Meta's Mega LLM scored a 97% on the Pathfinder-X dataset, indicating a need for an even more challenging benchmark.
Pathfinder-X2 contains 200,000 512x512 images along with 200,000 segmentation masks for those images.  Each image contains an assortment
of dashed-line "snakes" of varying length, and a model's task is to segment only the snake with a circle on one end.
Each image is meant to be fed in as a sequence,pixel-by-pixel, into a Large Language Model.

Explanation paper: URL

Based on the Pathfinder dataset by Drew Linsley, Alekh K Ashok, Lakshmi N Govindarajan, Rex Liu, and Thomas Serre.

!Sample input
!Sample label

---
annotations_creators:
- Tyler Suard
tags:
- language
- nlp
- llm
- long-range
size_categories:
- 100K<n<1M
---