Bridging the Data Provenance Gap Across Text, Speech and Video
Paper
•
2412.17847
•
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•
9
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vumichien
authored
a
paper
2 days ago
Post
2873
⛓ Evaluating Long Context #2: SCROLLS and ZeroSCROLLS
In this series of posts about tracing the history of long context evaluation, we started with Long Range Arena (LRA). Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation. But it wasn't introduced to evaluate LLMs, but rather the transformer architecture in general.
📜 The SCROLLS benchmark, introduced in 2022, addresses this gap in NLP/LLM research. SCROLLS challenges models with tasks that require reasoning over extended sequences (according to 2022 standards). So, what does it offer?
1️⃣ Long Text Focus: SCROLLS (unlike LRA) focus mainly on text and contain inputs with thousands of words, testing models' ability to synthesize information across lengthy documents.
2️⃣ Diverse Tasks: Includes summarization, question answering, and natural language inference across domains like literature, science, and business.
3️⃣ Unified Format: All datasets are available in a text-to-text format, facilitating easy evaluation and comparison of models.
Building on SCROLLS, ZeroSCROLLS takes long text evaluation to the next level by focusing on zero-shot learning. Other features include:
1️⃣ New Tasks: Introduces tasks like sentiment aggregation and sorting book chapter summaries.
2️⃣ Leaderboard: A live leaderboard encourages continuous improvement and competition among researchers.
💡 What are some other landmark benchmarks in the history of long context evaluation? Feel free to share your thoughts and suggestions in the comments.
- SCROLLS Paper: SCROLLS: Standardized CompaRison Over Long Language Sequences (2201.03533)
- ZeroSCROLLS Paper: ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding (2305.14196)
In this series of posts about tracing the history of long context evaluation, we started with Long Range Arena (LRA). Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation. But it wasn't introduced to evaluate LLMs, but rather the transformer architecture in general.
📜 The SCROLLS benchmark, introduced in 2022, addresses this gap in NLP/LLM research. SCROLLS challenges models with tasks that require reasoning over extended sequences (according to 2022 standards). So, what does it offer?
1️⃣ Long Text Focus: SCROLLS (unlike LRA) focus mainly on text and contain inputs with thousands of words, testing models' ability to synthesize information across lengthy documents.
2️⃣ Diverse Tasks: Includes summarization, question answering, and natural language inference across domains like literature, science, and business.
3️⃣ Unified Format: All datasets are available in a text-to-text format, facilitating easy evaluation and comparison of models.
Building on SCROLLS, ZeroSCROLLS takes long text evaluation to the next level by focusing on zero-shot learning. Other features include:
1️⃣ New Tasks: Introduces tasks like sentiment aggregation and sorting book chapter summaries.
2️⃣ Leaderboard: A live leaderboard encourages continuous improvement and competition among researchers.
💡 What are some other landmark benchmarks in the history of long context evaluation? Feel free to share your thoughts and suggestions in the comments.
- SCROLLS Paper: SCROLLS: Standardized CompaRison Over Long Language Sequences (2201.03533)
- ZeroSCROLLS Paper: ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding (2305.14196)
prithivMLmods
posted
an
update
6 days ago
Post
3992
QwQ Edge Gets a Small Update..! 💬
try now: prithivMLmods/QwQ-Edge
🚀Now, you can use the following commands for different tasks:
🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)
💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning
Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge
try now: prithivMLmods/QwQ-Edge
🚀Now, you can use the following commands for different tasks:
🖼️ @image 'prompt...' → Generates an image
🔉@tts1 'prompt...' → Generates speech in a female voice
🔉 @tts2 'prompt...' → Generates speech in a male voice
🅰️@text 'prompt...' → Enables textual conversation (If not specified, text-to-text generation is the default mode)
💬Multimodality Support : prithivMLmods/Qwen2-VL-OCR-2B-Instruct
💬For text generation, the FastThink-0.5B model ensures quick and efficient responses, prithivMLmods/FastThink-0.5B-Tiny
💬Image Generation: sdxl lightning model, SG161222/RealVisXL_V4.0_Lightning
Github: https://github.com/PRITHIVSAKTHIUR/QwQ-Edge
graph TD
A[User Interface] --> B[Chat Logic]
B --> C{Command Type}
C -->|Text| D[FastThink-0.5B]
C -->|Image| E[Qwen2-VL-OCR-2B]
C -->|@image| F[Stable Diffusion XL]
C -->|@tts| G[Edge TTS]
D --> H[Response]
E --> H
F --> H
G --> H
1024m
updated
a
collection
11 days ago
danylo-boiko
authored
a
paper
11 days ago
timothycdc
authored
a
paper
11 days ago
prithivMLmods
posted
an
update
12 days ago
Post
4752
o3-Mini and Deepseek R1
Worked out with some famous and weird examples.
🔥Blog: https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1
Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.
example 1: o3 Mini , example 2: Deepseek R1
Q2 : https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1#q2--web-solar-system-explorer
Worked out with some famous and weird examples.
🔥Blog: https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1
Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.
example 1: o3 Mini , example 2: Deepseek R1
Q2 : https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1#q2--web-solar-system-explorer
- 1 reply
prithivMLmods
posted
an
update
16 days ago
Post
5115
Deepswipe by
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.
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. Deepseek🐬🗿
Everything is now in recovery. 📉📈
.
.
.
. Deepseek🐬🗿
Everything is now in recovery. 📉📈
-
- 4 replies
prithivMLmods
posted
an
update
25 days ago
Post
3713
Q'n' Sketches ❤️🔥
🖼️ Adapters:
- Qs : strangerzonehf/Qs-Sketch
- Qd : strangerzonehf/Qd-Sketch
- Qx : strangerzonehf/Qx-Art
- Qc : strangerzonehf/Qc-Sketch
- Bb : strangerzonehf/Bg-Bag
🐍 Collection : strangerzonehf/q-series-sketch-678e3503bf3a661758429717
🔗Page : https://huggingface.co/strangerzonehf
.
.
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@prithivMLmods 🤗
🖼️ Adapters:
- Qs : strangerzonehf/Qs-Sketch
- Qd : strangerzonehf/Qd-Sketch
- Qx : strangerzonehf/Qx-Art
- Qc : strangerzonehf/Qc-Sketch
- Bb : strangerzonehf/Bg-Bag
🐍 Collection : strangerzonehf/q-series-sketch-678e3503bf3a661758429717
🔗Page : https://huggingface.co/strangerzonehf
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@prithivMLmods 🤗
sarahooker
authored
6
papers
25 days ago
Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress
Paper
•
2408.14960
•
Published
Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning
Paper
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2410.10801
•
Published
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Paper
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2411.19799
•
Published
•
11
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation
Paper
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2412.03304
•
Published
•
17
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
Paper
•
2406.03368
•
Published
Bridging the Data Provenance Gap Across Text, Speech and Video
Paper
•
2412.17847
•
Published
•
9
prithivMLmods
posted
an
update
29 days ago
Post
3093
ChemQwen-vL [ Qwen for Chem Vision ] 🧑🏻🔬
🧪Model : prithivMLmods/ChemQwen-vL
📝ChemQwen-vL is a vision-language model fine-tuned based on the Qwen2VL-2B Instruct model. It has been trained using the International Chemical Identifier (InChI) format for chemical compounds and is optimized for chemical compound identification. The model excels at generating the InChI and providing descriptions of chemical compounds based on their images. Its architecture operates within a multi-modal framework, combining image-text-text capabilities. It has been fine-tuned using datasets from: https://iupac.org/projects/
📒Colab Demo: https://tinyurl.com/2pn8x6u7, Collection : https://tinyurl.com/2mt5bjju
Inference with the documentation is possible with the help of the ReportLab library. https://pypi.org/project/reportlab/
🤗: @prithivMLmods
🧪Model : prithivMLmods/ChemQwen-vL
📝ChemQwen-vL is a vision-language model fine-tuned based on the Qwen2VL-2B Instruct model. It has been trained using the International Chemical Identifier (InChI) format for chemical compounds and is optimized for chemical compound identification. The model excels at generating the InChI and providing descriptions of chemical compounds based on their images. Its architecture operates within a multi-modal framework, combining image-text-text capabilities. It has been fine-tuned using datasets from: https://iupac.org/projects/
📒Colab Demo: https://tinyurl.com/2pn8x6u7, Collection : https://tinyurl.com/2mt5bjju
Inference with the documentation is possible with the help of the ReportLab library. https://pypi.org/project/reportlab/
🤗: @prithivMLmods
- 1 reply
shayekh
updated
a
Space
about 1 month ago
shayekh
updated
a
collection
about 1 month ago
Cartinoe5930
authored
a
paper
about 1 month ago
Aryan8912
updated
a
collection
about 1 month ago