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|---|---|---|---|---|---|---|---|---|---|
HoangHa/processed_lima | 2023-09-07T10:07:56.000Z | [
"region:us"
] | HoangHa | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 2850866
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download_size: 1679958
dataset_size: 2850866
---
# Dataset Card for "processed_lima"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tverous/anli-amr-amrlib | 2023-09-07T10:11:24.000Z | [
"region:us"
] | Tverous | null | null | null | 0 | 0 | ---
dataset_info:
features:
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- name: premise
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'0': entailment
'1': neutral
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- name: reason
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download_size: 20999544
dataset_size: 61840809
---
# Dataset Card for "anli-amr-amrlib"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Satya25/cobol-to-java-dataset | 2023-09-07T10:17:43.000Z | [
"region:us"
] | Satya25 | null | null | null | 0 | 0 | Entry not found |
timelessbeautyserumofficial/timelessbeautyserumofficial | 2023-09-07T10:50:26.000Z | [
"region:us"
] | timelessbeautyserumofficial | null | null | null | 0 | 0 | **Introducing [Timeless Beauty Age-Rewinding Serum](https://sites.google.com/view/timelessbeautyrevivaltheultima/home)
**
Are you in search of the fountain of youth for your skin? Look no further! Our revolutionary Timeless Beauty Age-Rewinding Serum is here to transform your skincare routine and help you achieve radiant, youthful skin that defies the passage of time.
[.png)](https://www.glitco.com/get-timeless-beauty-serum)
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[**Click Here To Shop Timeless Beauty Serum From Official Website**](https://www.glitco.com/get-timeless-beauty-serum)
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**Reversing Time: The Science Behind the Serum**
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Understanding the science behind our [**Timeless Beauty Serum**](https://medium.com/@zhaneraider/timeless-beauty-revival-the-ultimate-serum-83c09d36294) is the first step to comprehending its exceptional benefits. Our dedicated team of skincare experts and scientists have meticulously curated a formula that harnesses the power of nature and modern innovation. The serum’s unique blend includes potent antioxidants, nourishing vitamins, and deeply hydrating compounds.
**The Power of Ingredients**
At the heart of [**Timeless Beauty Serum**](https://club.vexanium.com/post/timeless-beauty-serum-rediscover-your-beauty-64f97f15616be36374734c27) lies a carefully curated selection of powerful ingredients, each chosen for its specific role in promoting youthful, radiant skin.
* **Hyaluronic Acid:** This moisture-binding superstar helps plump and hydrate the skin, reducing the appearance of fine lines and wrinkles. Hyaluronic acid acts like a sponge, holding up to 1000 times its weight in water, leaving your skin looking fresh and rejuvenated.
* **Vitamin C:** Renowned for its ability to brighten skin and even out skin tone, vitamin C is a must-have in any anti-aging regimen. [**Timeless Beauty Serum**](https://twitter.com/2timelessbeauty/status/1699676314465423796) contains a potent form of vitamin C that helps fade dark spots and promote a more radiant complexion.
* **Retinol:** Often referred to as the gold standard in anti-aging, retinol stimulates collagen production and accelerates cell turnover, resulting in smoother, firmer skin. The Timeless Beauty Serum incorporates a gentle yet effective form of retinol that minimizes irritation.
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* **Botanical Extracts:** To complement its powerhouse ingredients, the serum includes a variety of botanical extracts such as green tea, chamomile, and aloe vera, known for their soothing and antioxidant properties.
[**Click Here To Get It From Official Website**](https://www.glitco.com/get-timeless-beauty-serum)
[](https://www.glitco.com/get-timeless-beauty-serum)
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* Enhanced hydration and radiance
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* A Versatile Addition to Your Skincare Routine
[**Timeless Beauty Serum**](https://colab.research.google.com/drive/19me2ZKIjiUGkQCkzmrmdy9DPqFsGChxP?usp=sharing) is suitable for all skin types and can be seamlessly integrated into your existing skincare routine. Whether you use it as part of your morning routine to shield your skin from daily aggressors or as an essential step in your nighttime regimen to repair and rejuvenate, its lightweight texture ensures easy absorption.
[**Click Here To Shop Timeless Beauty Serum From Official Website**](https://www.glitco.com/get-timeless-beauty-serum)
**The Elixir of Radiance: Benefits You’ll Love**
* **Diminished Wrinkles:** Bid farewell to wrinkles and fine lines as our serum’s active ingredients penetrate deep within the skin, promoting collagen production and restoring elasticity.
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* **Intense Hydration:** Hyaluronic acid locks in moisture, ensuring your skin stays hydrated and plump throughout the day.
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**[Your Journey to Timelessness Starts Here](https://www.glitco.com/get-timeless-beauty-serum)**
**Step 1: Cleanse and Prepare**
Begin your regimen by cleansing your face with a gentle cleanser suited to your skin type. Pat your skin dry with a soft towel.
**Step 2: Apply the Serum**
Dispense a small amount of the [**Timeless Beauty Serum**](https://hackmd.io/@buytimelessbeautyserum26/timelessbeautyserumofficial) onto your fingertips. Gently massage the serum into your skin using upward motions, allowing it to fully absorb.
**Step 3: Moisturize and Protect**
Afterward, apply a gentle and hydrating moisturizer to seal in the advantages of the serum. Remember to put on a wide-ranging sunscreen to protect your skin from damaging ultraviolet rays.
**Join the [Timeless Beauty](https://sway.office.com/bkDX9TBXEHZ5BUwx?ref=Link) Movement**
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Experience the transformation for yourself. Embrace the power of [**Timeless Beauty Age-Rewinding Serum**](http://snaplant.com/question/timeless-beauty-serum-unlocking-your-beauty-secrets/) and embrace a future with skin that defies time.
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**The Fountain of Youth in a Bottle**
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[](https://www.glitco.com/get-timeless-beauty-serum)
**The Science Behind [Timeless Beauty](https://filmfreeway.com/TimelessBeautySerumUnlockingYourBeautySecrets)
**
The formulation of [**Timeless Beauty Serum**](https://www.bitsdujour.com/profiles/D3KCaF) is the result of rigorous scientific research and clinical testing. Skincare experts and dermatologists have worked tirelessly to create a product that not only meets but exceeds the expectations of users seeking lasting beauty.
**Conclusion**
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No longer do you need to search endlessly for the fountain of youth. Timeless Beauty Serum is here to help you rewrite the story of your skin, revealing a more radiant, youthful version of yourself. So, why wait? Embrace timeless beauty today and embark on a journey towards a more youthful you.
[**Click Here To Shop Timeless Beauty Serum From Official Website**](https://www.glitco.com/get-timeless-beauty-serum)
**Read More:**
[https://healthupdates2023.blogspot.com/2023/09/timeless-beauty-serum-unlocking-your.html](https://healthupdates2023.blogspot.com/2023/09/timeless-beauty-serum-unlocking-your.html)
[https://sites.google.com/view/timelessbeautyrevivaltheultima/home](https://sites.google.com/view/timelessbeautyrevivaltheultima/home)
[https://groups.google.com/g/timeless-beauty-skin/c/FHwcq4s0ji4](https://groups.google.com/g/timeless-beauty-skin/c/FHwcq4s0ji4)
[https://colab.research.google.com/drive/19me2ZKIjiUGkQCkzmrmdy9DPqFsGChxP?usp=sharing](https://colab.research.google.com/drive/19me2ZKIjiUGkQCkzmrmdy9DPqFsGChxP?usp=sharing)
[https://lookerstudio.google.com/reporting/446e1a64-d44d-4472-a8b7-235324fa5b88](https://lookerstudio.google.com/reporting/446e1a64-d44d-4472-a8b7-235324fa5b88)
[https://bookshop.org/wishlists/1427e2febf9c741c9b8ba28923f69b446fbb71f4](https://bookshop.org/wishlists/1427e2febf9c741c9b8ba28923f69b446fbb71f4)
[https://club.vexanium.com/post/timeless-beauty-serum-rediscover-your-beauty-64f97f15616be36374734c27](https://club.vexanium.com/post/timeless-beauty-serum-rediscover-your-beauty-64f97f15616be36374734c27)
[https://twitter.com/2timelessbeauty](https://twitter.com/2timelessbeauty)
[https://twitter.com/2timelessbeauty/status/1699676314465423796](https://twitter.com/2timelessbeauty/status/1699676314465423796)
[https://sway.office.com/bkDX9TBXEHZ5BUwx?ref=Link](https://sway.office.com/bkDX9TBXEHZ5BUwx?ref=Link)
[https://filmfreeway.com/TimelessBeautySerumUnlockingYourBeautySecrets](https://filmfreeway.com/TimelessBeautySerumUnlockingYourBeautySecrets)
[https://www.crunchbase.com/organization/timeless-beauty-serum-f8b6](https://www.crunchbase.com/organization/timeless-beauty-serum-f8b6)
[https://www.bitsdujour.com/profiles/D3KCaF](https://www.bitsdujour.com/profiles/D3KCaF)
[http://snaplant.com/question/timeless-beauty-serum-unlocking-your-beauty-secrets/](http://snaplant.com/question/timeless-beauty-serum-unlocking-your-beauty-secrets/)
[https://vimeo.com/861924350?share=copy](https://vimeo.com/861924350?share=copy)
[https://www.pinterest.com/pin/1151303092225145628](https://www.pinterest.com/pin/1151303092225145628)
[https://www.pinterest.com/pin/1151303092225145591/](https://www.pinterest.com/pin/1151303092225145591/)
[https://www.yepdesk.com/timeless-beauty-serum](https://www.yepdesk.com/timeless-beauty-serum)
[https://hackmd.io/@buytimelessbeautyserum26/timelessbeautyserumofficial](https://hackmd.io/@buytimelessbeautyserum26/timelessbeautyserumofficial)
[https://soundcloud.com/zhane-raider/timeless-beauty-revival-the-ultimate-serum](https://soundcloud.com/zhane-raider/timeless-beauty-revival-the-ultimate-serum)
[https://medium.com/@zhaneraider/timeless-beauty-revival-the-ultimate-serum-83c09d36294](https://medium.com/@zhaneraider/timeless-beauty-revival-the-ultimate-serum-83c09d36294)
[https://issuu.com/buytimelessbeautyserum2/docs/timeless\_beauty\_age](https://issuu.com/buytimelessbeautyserum2/docs/timeless_beauty_age)
[https://www.reddit.com/user/timelessbeautyserum/comments/16ca1ai/timeless\_beauty\_serum\_unlocking\_your\_beauty/](https://www.reddit.com/user/timelessbeautyserum/comments/16ca1ai/timeless_beauty_serum_unlocking_your_beauty/)
[https://www.ourboox.com/books/timeless-beauty-in-a-bottle-discover-the-serum/](https://www.ourboox.com/books/timeless-beauty-in-a-bottle-discover-the-serum/)
[https://radiance-reimagined-timeless-beauty-serum.jimdosite.com/](https://radiance-reimagined-timeless-beauty-serum.jimdosite.com/)
[https://timeless-beauty-in-a-bottle-discover-th.webflow.io/](https://timeless-beauty-in-a-bottle-discover-th.webflow.io/)
[https://timelessbeautyserumofficial.hashnode.dev/timeless-beauty-elixir-reveal-your-inner-glow](https://timelessbeautyserumofficial.hashnode.dev/timeless-beauty-elixir-reveal-your-inner-glow)
[https://devfolio.co/@timelessbeauty2](https://devfolio.co/@timelessbeauty2)
[https://devfolio.co/projects/timeless-beauty-serum-rediscover-your-beauty-d8a7](https://devfolio.co/projects/timeless-beauty-serum-rediscover-your-beauty-d8a7) |
tpoisonooo/antiseed-v0.0.1alpha0-feature | 2023-09-07T10:56:24.000Z | [
"region:us"
] | tpoisonooo | null | null | null | 0 | 0 | Entry not found |
dhenypatungka/kareena-kapoor | 2023-09-07T10:57:34.000Z | [
"region:us"
] | dhenypatungka | null | null | null | 0 | 0 | Entry not found |
MaryHunter9876/everhempzcbdgummies | 2023-09-07T11:18:03.000Z | [
"region:us"
] | MaryHunter9876 | null | null | null | 0 | 0 | Entry not found |
timelessbeautyskinserumshop/TimelessBeautySerum | 2023-09-07T11:19:29.000Z | [
"region:us"
] | timelessbeautyskinserumshop | null | null | null | 0 | 0 | In today's fast-paced world, everyone is in pursuit of timeless beauty. We all desire youthful, radiant skin that defies the effects of time. Enter the Timeless Beauty Serum - a revolutionary skincare product that promises to turn back the clock on your skin. In this article, we will delve deep into the world of Timeless Beauty Serum, exploring its ingredients, benefits, and how it can transform your skincare routine.
**[.png)](https://www.glitco.com/get-timeless-beauty-serum)**
**[Click Here To Shop Timeless Beauty Serum From Official Website](https://www.glitco.com/get-timeless-beauty-serum)**
----------------------------------------------------------------------------------------------------------------------
What is Timeless Beauty Serum?
------------------------------
Timeless Beauty Serum is a potent anti-aging serum designed to rejuvenate and revitalize your skin. Packed with powerful ingredients, this serum claims to diminish the signs of aging, leaving you with a youthful and radiant complexion.
The Science Behind Timeless Beauty Serum
Understanding the Ingredients
The secret behind the efficacy of Timeless Beauty Serum lies in its carefully selected ingredients. This serum typically includes:
Hyaluronic Acid (H2)
Hyaluronic Acid is a powerhouse ingredient known for its ability to hydrate and plump the skin. It helps in reducing fine lines and wrinkles, giving your skin a smoother appearance.
Retinol (H2)
Retinol, a derivative of Vitamin A, is celebrated for its skin-renewing properties. It promotes collagen production, which is crucial for maintaining skin elasticity.
Vitamin C (H3)
Vitamin C is a potent antioxidant that brightens the skin, fades dark spots, and protects it from environmental damage.
Peptides (H3)
Peptides stimulate collagen production and help in repairing damaged skin cells.
The Benefits of Using Timeless Beauty Serum
-------------------------------------------
1\. Wrinkle Reduction
The Timeless Beauty Serum is renowned for its ability to reduce the appearance of fine lines and wrinkles, making your skin look noticeably younger.
2\. Improved Hydration
With Hyaluronic Acid as a key ingredient, this serum ensures your skin remains well-hydrated, leaving it plump and supple.
3\. Even Skin Tone
Vitamin C in the serum helps in fading dark spots and promoting an even skin tone, giving you a natural radiance.
4\. Enhanced Elasticity
Retinol and peptides work together to boost collagen production, improving skin elasticity and firmness.
**[.png)](https://www.glitco.com/get-timeless-beauty-serum)**
**[Click Here To Shop Timeless Beauty Serum From Official Website](https://www.glitco.com/get-timeless-beauty-serum)**
----------------------------------------------------------------------------------------------------------------------
How to Incorporate Timeless Beauty Serum into Your Skincare Routine
-------------------------------------------------------------------
To make the most of Timeless Beauty Serum, follow these steps:
Step 1: Cleanse
Start with a gentle cleanser to remove dirt and impurities from your skin.
Step 2: Apply Serum
Take a few drops of Timeless Beauty Serum and gently massage it into your face and neck.
Step 3: Moisturize
Lock in the benefits of the serum with a moisturizer suitable for your skin type.
Step 4: Sun Protection
Finish your routine with a broad-spectrum sunscreen to shield your skin from harmful UV rays.
Real People, Real Results
-------------------------
Testimonials
"I've been using Timeless Beauty Serum for a month, and I can already see a significant reduction in my fine lines." - Sarah, 42
"This serum has given me the confidence to go makeup-free. My skin looks and feels amazing!" - David, 35
### Conclusion
Timeless Beauty Serum is a game-changer in the world of skincare. Its powerful ingredients, combined with a simple skincare routine, can help you achieve the timeless beauty you've always desired. Say goodbye to the signs of aging and embrace a youthful, radiant complexion.
**[.png)](https://www.glitco.com/get-timeless-beauty-serum)**
**[Click Here To Shop Timeless Beauty Serum From Official Website](https://www.glitco.com/get-timeless-beauty-serum)**
----------------------------------------------------------------------------------------------------------------------
FAQs
----
Is Timeless Beauty Serum suitable for all skin types?
Yes, Timeless Beauty Serum is formulated to be gentle and suitable for all skin types.
How long does it take to see results?
Results may vary, but many users report visible improvements within a few weeks of regular use.
Can I use Timeless Beauty Serum with other skincare products?
Yes, you can incorporate Timeless Beauty Serum into your existing skincare routine for enhanced results.
[https://colab.research.google.com/drive/1uvja8lQOllt09yP56eqqAvnTq53wH5Vw#scrollTo=UFjxdkLfDgBm](https://colab.research.google.com/drive/1uvja8lQOllt09yP56eqqAvnTq53wH5Vw#scrollTo=UFjxdkLfDgBm)
[https://sites.google.com/view/timeless-beauty-serum-pick/home](https://sites.google.com/view/timeless-beauty-serum-pick/home)
[https://lookerstudio.google.com/reporting/2fa24fc0-2b84-41df-a6a9-c2b00a3fca28/page/9xxbD](https://lookerstudio.google.com/reporting/2fa24fc0-2b84-41df-a6a9-c2b00a3fca28/page/9xxbD)
[https://groups.google.com/g/timeless-beauty-serum-pick/c/6dat6lXwseg](https://groups.google.com/g/timeless-beauty-serum-pick/c/6dat6lXwseg)
[https://www.facebook.com/people/Timeless-Beauty-Serum-Review/61550686246706/](https://www.facebook.com/people/Timeless-Beauty-Serum-Review/61550686246706/)
[https://timeless-beauty-skin-serum-ce647d.webflow.io/](https://timeless-beauty-skin-serum-ce647d.webflow.io/)
[https://bookshop.org/wishlists/90b6700080d7734753de014ccd9dbdcbc3767782](https://bookshop.org/wishlists/90b6700080d7734753de014ccd9dbdcbc3767782)
[https://www.youtube.com/watch?v=y8Lh5GaUZKs](https://www.youtube.com/watch?v=y8Lh5GaUZKs)
[https://www.facebook.com/groups/860593642305254](https://www.facebook.com/groups/860593642305254)
[https://timelessbeautyserumpick.contently.com/](https://timelessbeautyserumpick.contently.com/)
[https://soundcloud.com/buytimelessbeautyserum/timeless-beauty-serum-ageless-allure-timeless-beauty-skin-serum-reviews-720p?](https://soundcloud.com/buytimelessbeautyserum/timeless-beauty-serum-ageless-allure-timeless-beauty-skin-serum-reviews-720p?)
[https://devfolio.co/@TimelessBeauty3](https://devfolio.co/@TimelessBeauty3)
[https://timeless-beauty-serum-pick.company.site/](https://timeless-beauty-serum-pick.company.site/) |
MaryHunter9876/everhepmzcbdgummiescanada | 2023-09-07T11:20:28.000Z | [
"region:us"
] | MaryHunter9876 | null | null | null | 0 | 0 | Entry not found |
MaryHunter9876/everhempzcbdgummiescanadareviews | 2023-09-07T11:21:41.000Z | [
"region:us"
] | MaryHunter9876 | null | null | null | 0 | 0 | Entry not found |
sumansaurav18/demo1-dataset | 2023-09-07T11:28:40.000Z | [
"region:us"
] | sumansaurav18 | null | null | null | 0 | 0 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
gpadam/training_searches_cleaned_2023.09.07 | 2023-09-07T11:28:53.000Z | [
"region:us"
] | gpadam | null | null | null | 0 | 0 | Entry not found |
OloriBern/FLDE | 2023-10-09T09:19:05.000Z | [
"region:us"
] | OloriBern | null | null | null | 0 | 0 | Entry not found |
Edmon02/opus | 2023-09-07T11:39:14.000Z | [
"region:us"
] | Edmon02 | null | null | null | 0 | 0 | Entry not found |
bipulparua/llm-lotr-test | 2023-09-07T11:45:24.000Z | [
"region:us"
] | bipulparua | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2196528.0
num_examples: 268
- name: test
num_bytes: 245880.0
num_examples: 30
download_size: 1128634
dataset_size: 2442408.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "llm-lotr-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TrojanMade/GameMeneer | 2023-09-07T12:00:45.000Z | [
"license:openrail",
"region:us"
] | TrojanMade | null | null | null | 0 | 0 | ---
license: openrail
---
|
TamelaMannWeightLossKetoGummies/TamelaMannWeightLossGummies | 2023-09-07T12:12:10.000Z | [
"region:us"
] | TamelaMannWeightLossKetoGummies | null | null | null | 0 | 0 | <h3><span style="background-color: #ffff99;"><strong>Our Official Facebook Pages ⇒</strong></span></h3>
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<h2><span style="color: #000000; background-color: #ffff00;"><a style="color: #000000; background-color: #ffff00;" href="https://www.facebook.com/TamelaMannWeightLossKeto/"><strong>What are the Rewards of Tamela Mann Weight Loss Gummies?</strong></a></span></h2>
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paidninja/kpargi_project | 2023-09-07T12:13:29.000Z | [
"license:mit",
"region:us"
] | paidninja | null | null | null | 0 | 0 | ---
license: mit
---
|
rejuvaskintag/Rejuva-Skin-Tag-Remover | 2023-09-07T12:40:44.000Z | [
"region:us"
] | rejuvaskintag | null | null | null | 0 | 0 | <div id="story-content-8f6a0bde-768b-4d6d-97a4-a3af698979a3" class="story-cards">
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<p>The lotion can help eliminate blemishes and improve the appearance and health of your skin by adding shine and enhancing its current condition. This solution is highly recommended as it is both cost-effective and painless for customers who wish to remove patches and moles.</p>
<h2 style="text-align: left;"><strong>What is Rejuva Skin Tag Remover?</strong></h2>
<p>The ingredients in <a href="https://rejuva-skin-tag-remover-official.jimdosite.com/">Rejuva Skin Tag Remover</a> have a positive impact on the skin and are primarily intended to remove skin tags, moles, and blemishes. The solution effectively addresses all the problems at once. Having an excessive amount of moles, blemishes, and other facial issues can make us self-conscious about our appearance when interacting with others. It is essential to be attentive to all of these factors as the treatment for removing moles may have adverse effects. Choosing natural treatments over complex ones is recommended. Rejuva Skin Tag Remover is a safe and natural solution for facial flaws and moles.</p>
<p>The serum contains important elements that can enhance skin smoothness. It also promotes better cell growth, leading to healthier and more glowing skin. The company has used high-quality ingredients to promote healthy collagen levels, improve blood flow, and enhance skin for those of us who desire a radiant complexion. The reviews for <a href="https://sketchfab.com/3d-models/rejuva-skin-tag-remover-scam-2023-is-it-works-005cca59e99848f4b42fd25ba2d7b7ce">Rejuva Skin Tag Remover</a> can provide us with a better understanding of how effective it is for the skin.</p>
<p>This product is amazing as it provides healthy nutrients to the skin and helps to remove spots, freckles, and marks from the skin. Let's learn more about it.</p>
<p><strong><span style="font-size: large;"><a href="https://sale365day.com/get-rejuva-skin-tag" target="_blank" rel="nofollow noopener">Limited Discount: Get Rejuva Skin Tag Remover at 70% off on the official website!</a></span></strong></p>
<h2 style="text-align: left;"><strong>How does it work?</strong></h2>
<p>For best results, it is recommended to apply this serum to your skin every day to help fight against blemishes, skin infections, and other skin disorders. The serum targets the root cause of skin problems and supports the production of white blood cells. The product quickly absorbs into the skin, giving you a radiant complexion and promoting overall good health. If you stop using the product, you may not achieve the desired results. This is a high-quality product that helps promote youthful, beautiful skin by providing rich nutrients and reducing wrinkles. It is a potent solution that offers ample assistance.</p>
<p>It can be used as a moisturizing lotion that enhances the skin's natural defenses against skin issues like wrinkles. Customers who buy from the official website can receive a complimentary sample of the high-quality anti-aging product. This cream contains a pure peptide that helps improve skin issues and makes users look younger. It has become popular due to its ability to produce visible results after just a few applications.</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/get-rejuva-skin-tag"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEixmric__QxVf0DfEeNEMbK4JqgB_DYxrtnXyw_8-y-8RBP0WWaED8P9geerNIuZWyudLozXjb1kpisPkmgqAOtpj566n2VO2-V0D9KBAznufPvoJbSd26OBqUh_5zPRRBEVgenhG_uMTHEZEuy0m22Rz90IVjbsQOI23zvPywAe-zlLfaYNidTECKU/w640-h376/efeggggg.JPG" alt="" width="640" height="376" border="0" data-original-height="456" data-original-width="776" /></a></div>
<h2 style="text-align: left;"><strong>Rejuva Skin Tag Remover Ingrendients</strong></h2>
<p>The <a href="https://groups.google.com/g/rejuva-skin-tag-remover-official/c/_SkHCvDcG9o">Rejuva Skin Tag Remover</a> Serum is made with natural oils and a few reliable ingredients. The product's ingredients are all-natural and have undergone thorough testing. The following list includes some of the ingredients that were used to make the product:</p>
<p><strong>Sanguinaria Canadensis:</strong> Native Americans have used this herbaceous perennial plant for ages to treat a variety of illnesses. It can be found growing throughout most of North America. <a href="https://rejuvaskintagremoverusa.bandcamp.com/track/rejuva-skin-tag-remover-eliminate-mole-skin-tag-new-update-2023-read-ingredients">Rejuva Skin Tag Remover</a> contains sanguinaria which helps to eliminate skin tags by encouraging white blood cells to continue moving to the affected area for treatment.</p>
<p><strong>Zincum Muriaticum:</strong> The mineral zincum muriaticum, found in the Earth's crust, possesses antibiotic and disinfectant properties. Applying pressure to a scar can cause skin damage such as the development of a mole, skin tag, or another blemish that may scab over. This may trigger the healing process for the removal of the scar.</p>
<p><strong>Aloe vera:</strong> Aloe vera is a substance that can help to calm the skin, heal wounds, and reduce inflammation. Additionally, it can moisturize the skin, soothe irritation, and enhance its texture.</p>
<p><strong>Avocado Oil:</strong> This product is great for fixing dry skin as it effectively moisturizes the skin, diminishes wrinkles, enhances flexibility, and fights against free radical damage.</p>
<p><strong>Hyaluronic Acid:</strong> For ages, an essential element has been used to treat skin problems. This element is commonly found in different skin-care products such as face wash, creams, gels, and serums. This serum contains a significant amount of our ingredient, which is known to function like water for your skin and enhances the serum's effects.</p>
<p><strong><span style="font-size: large;"><a href="https://sale365day.com/get-rejuva-skin-tag" target="_blank" rel="nofollow noopener">Click to buy Rejuva Skin Tag Remover today from the company’s official website!</a></span></strong></p>
<h2 style="text-align: left;"><strong>What are the benefits of using Rejuva Skin Tag Remover?</strong></h2>
<ul>
<li>This product naturally eliminates various skin conditions such as skin tags, warts (big and small), and moles.</li>
<li>The product comprises natural ingredients that have been utilized since ancient times to treat these skin conditions.</li>
<li>This option is both affordable and free from side effects, and it is guaranteed to produce results.</li>
<li>This product is safe for use anywhere on the body and won't cause any allergic reactions.</li>
<li>This product can remove skin tags without any discomfort.</li>
<li>This gentle and natural formula can help you achieve clear skin without the need for surgery.</li>
<li>Taking this medication decreases the likelihood of experiencing negative effects that can arise from surgeries or other non-prescription drugs.</li>
</ul>
<h2 style="text-align: left;"><strong>The correct way of using Rejuva Skin Tag Remover serum</strong></h2>
<p>To use <a href="https://groups.google.com/g/rejuva-skin-tag-remover-official/c/tav1MoYGR_c">Rejuva Skin Tag Remover</a> serum, simply follow the instructions provided by the product manufacturers for easy application. The directions provided are clear and trustworthy for all users. The serum is designed to aid in removing skin tags and blemishes naturally by facilitating changes in your skin, without requiring any excessive force. </p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/get-rejuva-skin-tag"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCvI_OPyjdQ--0BAfPCUg1jlvz6dzZ9jtnTqLwroC9Fo6NSeZT_pdl2k4Cla3tBPwHr9MXwPzexOhjyXBUQrJ8fnSIeBt2UV3LQqCJoofXJaFj2KMnmii0EDsLL_8JafMuKAgAYARHmeMAEhL48JDv9JBJzePfJykp6r9tQ8aGj0wlGEBTq9vyqe2L/w640-h474/efsddef.JPG" alt="" width="640" height="474" border="0" data-original-height="583" data-original-width="788" /></a></div>
<h2 style="text-align: left;"><strong>How to Use Rejuva Skin Tag Remover?</strong></h2>
<p>The skin tag removal offered by the company comes in a serum form that is a gel-like liquid and is simple to apply topically. It can help to prevent the spread of skin issues. To fully cover the wart, follow these steps: Apply the cream directly on the wart and massage it for a minute. Then, wait for it to dry. If you want to benefit from your prescription, make sure to use it twice a day and keep the bandage on for a minimum of eight hours. To observe any noticeable effects, please make sure to repeat the procedure once per week for a minimum of 12 weeks.</p>
<h2 style="text-align: left;"><strong>Is it Legitimate or Fraud?</strong></h2>
<p>You can buy the <a href="https://www.provenexpert.com/rejuva-skin-tag-remover-honest-warning-must-read-this/">Rejuva Skin Tag Remover</a> Serum from its <a href="https://sale365day.com/get-rejuva-skin-tag">official website</a>. The treatment will make your skin look younger and free of wrinkles. This product is reliable and has no negative aspects. It is a cost-effective solution for removing patches and moles, without causing harm to the users. Rejuva Skin follows Good Manufacturing Practices and adheres to high standards of business ethics to produce approved high-quality products. To get in touch with the creators of <a href="https://lookerstudio.google.com/reporting/512b8c48-e954-45be-a8be-50c285620bcd/page/XRzbD">Rejuva Skin Tag Remover</a>, simply visit The official website.</p>
<h2 style="text-align: left;"><strong>How Long Should You Use Rejuva Skin Tag Remover Serum?</strong></h2>
<p><a href="https://soundcloud.com/rejuva-skin-tag-remover-524618945/rejuva-skin-tag-remover-reviews-fake-customer-complaints-best-mole-removal">Rejuva Skin Tag Remover</a> only uses all-natural ingredients without any added chemicals for faster results. Although supplements containing chemical compounds may produce quick results with Rejuva Skin Tag Remover, they can also cause side effects.</p>
<p>Unlike supplements that contain harmful toxins, Rejuva Skin Tag Remover works naturally on the body without any additives, preservatives, colors, or stimulants. However, it may take some time to see results.</p>
<p>Based on reviews for <a href="https://rejuvaskintagremover.godaddysites.com/">Rejuva Skin Tag Remover</a>, applying the product consistently for a few days can help to easily remove skin tags. If you use Rejuva Skin Tag Remover according to the recommended dosage period, you can achieve long-lasting results, which may appear to be permanent. Although the effects of this supplement may vary from person to person based on their body composition, all users of this supplement can still achieve healthier skin.</p>
<p><strong><span style="font-size: large;"><a href="https://sale365day.com/get-rejuva-skin-tag" target="_blank" rel="nofollow noopener">(BEST PRICE ON ONLINE) For a Limited Time, Rejuva Skin Tag Remover Is On Sale!</a></span></strong></p>
<h2 style="text-align: left;"><strong>Rejuva Skin Remover Serum Side Effects</strong></h2>
<p>This serum will make you look younger by reducing the appearance of wrinkles, blemishes, and fine lines. It contains nutrients that help in achieving this effect. The serum is safe to use and has no negative effects. The manufacturers are not allowed to use any GMOs, harmful fillers, or other synthetic materials that could cause harm to your health, skin, or hair. Rejuva Skin Tag Remover Serum cures the problem by deeply penetrating the skin and produces longer-lasting results than other similar products with short-lived results.</p>
<h2 style="text-align: left;"><strong>Pricing</strong></h2>
<p>You can find various discount bundles on the <a href="https://lookerstudio.google.com/reporting/512b8c48-e954-45be-a8be-50c285620bcd/page/XRzbD">Rejuva Skin Tag Remover</a> website.</p>
<ul>
<li>One bottle of Amaros Skin Tag Remover: $69.95</li>
<li>Two bottles of Amaros Skin Tag Remover + 1 Free: $59.95 each</li>
<li>Three bottles of Amaros Skin Tag Remover + 2 Free: $39.95 each</li>
</ul>
<h2 style="text-align: left;"><strong>Does the Company Offer a Money Back Guarantee?</strong></h2>
<p>Rejuva's founder is very confident in their solution's ability to eliminate any issues. They offer a money-back guarantee on all orders, regardless of the product selected. According to the authorized website, all orders are eligible for a 30-day money-back guarantee. You can request a full refund if you are not satisfied with the product after 30 days if it does not show any progress, or if you experience any negative effects from using it. If you bought Rejuva, you can ask the producer for a refund within thirty days after the purchase. They will refund you the full amount of your purchase, except for the shipping costs.</p>
<h2 style="text-align: left;"><strong>Where to Buy Rejuva Skin Tag Remover?</strong></h2>
<p>To purchase Rejuva Skin Tag Remover, <a href="https://sale365day.com/get-rejuva-skin-tag">visit the official Limitless website</a> where you will need to fill out a form and select the appropriate package. This is available for anyone to do. Once you have paid for the product, the company will begin the shipping process.</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/get-rejuva-skin-tag"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjdTkq81FmCVaQwu5xNRVzs-_jH8LWnbg6L_ebNWToSD0Ob-RpCgoo7cBhncq_73zWeeYaHhCht08C1mg7Zp5hkjxuAPDs8wxNEsbROVsNDm8M6xUHWQDIslpeMfeMkvDNWgJIj3_czaLZXWWjdn5BXDEWfNn7HW90Su6UY0XLIcxUfWe8DH5MGS9w7/w640-h252/oie_HVeSJu8fa52I.jpg" alt="" width="640" height="252" border="0" data-original-height="417" data-original-width="1060" /></a></div>
<h2 style="text-align: left;"><strong>Rejuva Skin Tag Remover Reviews - Final Words</strong></h2>
<p><a href="https://infogram.com/rejuva-skin-tag-remover-reviews-scam-or-real-mole-and-skin-tag-corrector-serum-1hnp27m9jg8mn2g">Rejuva Skin Tag Remover</a> is a highly effective product that can eliminate skin tags, moles, and other types of skin growths right from its first application. The skincare product has had a significant impact on the industry by improving the appearance of people's skin in just 30 days of use, resulting in clearer and healthier-looking skin for many.<a href="https://rejuva-skin-tag-remover-report.clubeo.com/page/is-rejuva-skin-tag-remover-really-correct-skin-tag-and-mole-or-fake-serum.html">Rejuva Skin Tag Remover</a> only contains natural ingredients and no chemicals, toxins, or additives. Therefore, it does not have any side effects.</p>
<p><strong>Read More:</strong></p>
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indonlp/nusaparagraph_emot | 2023-09-07T13:05:53.000Z | [
"license:apache-2.0",
"region:us"
] | indonlp | Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages.
We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej).
For the emotion recognition task, we cover the 6 basic emotions (Ekman, 1992): fear, disgusted, sad, happy, angry, and surprise, and an additional emotion label: shame (Poulson and of Tasmania. School of Management, 2000. | @unpublished{anonymous2023nusawrites:,
title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages},
author={Anonymous},
journal={OpenReview Preprint},
year={2023},
note={anonymous preprint under review}
} | null | 0 | 0 | ---
license: apache-2.0
---
|
indonlp/nusaparagraph_rhetoric | 2023-09-07T13:05:19.000Z | [
"license:apache-2.0",
"region:us"
] | indonlp | Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages.
We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej).
For the rhetoric mode classification task, we cover 5 rhetoric modes, i.e., narrative, persuasive, argumentative, descriptive, and expository. | @unpublished{anonymous2023nusawrites:,
title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages},
author={Anonymous},
journal={OpenReview Preprint},
year={2023},
note={anonymous preprint under review}
} | null | 0 | 0 | ---
license: apache-2.0
---
|
indonlp/nusaparagraph_topic | 2023-09-07T13:04:43.000Z | [
"license:apache-2.0",
"region:us"
] | indonlp | Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages.
We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej).
For the topic modeling task, we cover 8 topics, i.e., food \& beverages, sports, leisure, religion, culture \& heritage, a slice of life, technology, and business. | @unpublished{anonymous2023nusawrites:,
title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages},
author={Anonymous},
journal={OpenReview Preprint},
year={2023},
note={anonymous preprint under review}
} | null | 0 | 0 | ---
license: apache-2.0
---
|
odunola/sentimenttweets | 2023-09-07T14:21:13.000Z | [
"license:apache-2.0",
"region:us"
] | odunola | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
bitadin/attributes-v5 | 2023-09-08T07:35:50.000Z | [
"region:us"
] | bitadin | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 102835054
num_examples: 140310
download_size: 59172105
dataset_size: 102835054
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "attributes-v5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16 | 2023-09-07T13:34:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16](https://huggingface.co/dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-07T13:33:12.339824](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16/blob/main/results_2023-09-07T13-33-12.339824.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.45715117494244945,\n\
\ \"acc_stderr\": 0.03509698630949401,\n \"acc_norm\": 0.46149986826009826,\n\
\ \"acc_norm_stderr\": 0.035082198567284846,\n \"mc1\": 0.2864137086903305,\n\
\ \"mc1_stderr\": 0.01582614243950236,\n \"mc2\": 0.43403758756918054,\n\
\ \"mc2_stderr\": 0.014103822022452518\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.48464163822525597,\n \"acc_stderr\": 0.014604496129394908,\n\
\ \"acc_norm\": 0.5409556313993175,\n \"acc_norm_stderr\": 0.014562291073601226\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5871340370444135,\n\
\ \"acc_stderr\": 0.0049134290105590705,\n \"acc_norm\": 0.7873929496116312,\n\
\ \"acc_norm_stderr\": 0.004083157276012497\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5037037037037037,\n\
\ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.5037037037037037,\n\
\ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.4605263157894737,\n \"acc_stderr\": 0.04056242252249033,\n\
\ \"acc_norm\": 0.4605263157894737,\n \"acc_norm_stderr\": 0.04056242252249033\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.46,\n\
\ \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.46037735849056605,\n \"acc_stderr\": 0.03067609659938918,\n\
\ \"acc_norm\": 0.46037735849056605,\n \"acc_norm_stderr\": 0.03067609659938918\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.4305555555555556,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542126\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n\
\ \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.3988439306358382,\n\
\ \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.04220773659171453,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04220773659171453\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3829787234042553,\n \"acc_stderr\": 0.031778212502369216,\n\
\ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.031778212502369216\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\
\ \"acc_stderr\": 0.043036840335373146,\n \"acc_norm\": 0.2982456140350877,\n\
\ \"acc_norm_stderr\": 0.043036840335373146\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\
\ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708624,\n \"\
acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708624\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\
\ \"acc_stderr\": 0.042407993275749234,\n \"acc_norm\": 0.3412698412698413,\n\
\ \"acc_norm_stderr\": 0.042407993275749234\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.49032258064516127,\n\
\ \"acc_stderr\": 0.02843867799890955,\n \"acc_norm\": 0.49032258064516127,\n\
\ \"acc_norm_stderr\": 0.02843867799890955\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n\
\ \"acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.038154943086889305,\n\
\ \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.038154943086889305\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.47474747474747475,\n \"acc_stderr\": 0.03557806245087314,\n \"\
acc_norm\": 0.47474747474747475,\n \"acc_norm_stderr\": 0.03557806245087314\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836184,\n\
\ \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836184\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4230769230769231,\n \"acc_stderr\": 0.025049197876042338,\n\
\ \"acc_norm\": 0.4230769230769231,\n \"acc_norm_stderr\": 0.025049197876042338\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.4369747899159664,\n \"acc_stderr\": 0.03221943636566196,\n \
\ \"acc_norm\": 0.4369747899159664,\n \"acc_norm_stderr\": 0.03221943636566196\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804724,\n \"\
acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804724\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6036697247706422,\n \"acc_stderr\": 0.020971469947900532,\n \"\
acc_norm\": 0.6036697247706422,\n \"acc_norm_stderr\": 0.020971469947900532\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.21296296296296297,\n \"acc_stderr\": 0.027920963147993656,\n \"\
acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.027920963147993656\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.4803921568627451,\n \"acc_stderr\": 0.03506612560524866,\n \"\
acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.03506612560524866\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.5907172995780591,\n \"acc_stderr\": 0.032007041833595914,\n \
\ \"acc_norm\": 0.5907172995780591,\n \"acc_norm_stderr\": 0.032007041833595914\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5336322869955157,\n\
\ \"acc_stderr\": 0.033481800170603065,\n \"acc_norm\": 0.5336322869955157,\n\
\ \"acc_norm_stderr\": 0.033481800170603065\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n\
\ \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6115702479338843,\n \"acc_stderr\": 0.04449270350068382,\n \"\
acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.04449270350068382\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5370370370370371,\n\
\ \"acc_stderr\": 0.04820403072760628,\n \"acc_norm\": 0.5370370370370371,\n\
\ \"acc_norm_stderr\": 0.04820403072760628\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.4662576687116564,\n \"acc_stderr\": 0.039194155450484096,\n\
\ \"acc_norm\": 0.4662576687116564,\n \"acc_norm_stderr\": 0.039194155450484096\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.5728155339805825,\n \"acc_stderr\": 0.04897957737781168,\n\
\ \"acc_norm\": 0.5728155339805825,\n \"acc_norm_stderr\": 0.04897957737781168\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6623931623931624,\n\
\ \"acc_stderr\": 0.030980296992618558,\n \"acc_norm\": 0.6623931623931624,\n\
\ \"acc_norm_stderr\": 0.030980296992618558\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.017268607560005783,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.017268607560005783\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5144508670520231,\n \"acc_stderr\": 0.026907849856282542,\n\
\ \"acc_norm\": 0.5144508670520231,\n \"acc_norm_stderr\": 0.026907849856282542\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5098039215686274,\n \"acc_stderr\": 0.028624412550167958,\n\
\ \"acc_norm\": 0.5098039215686274,\n \"acc_norm_stderr\": 0.028624412550167958\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5691318327974276,\n\
\ \"acc_stderr\": 0.028125340983972714,\n \"acc_norm\": 0.5691318327974276,\n\
\ \"acc_norm_stderr\": 0.028125340983972714\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.4783950617283951,\n \"acc_stderr\": 0.027794760105008746,\n\
\ \"acc_norm\": 0.4783950617283951,\n \"acc_norm_stderr\": 0.027794760105008746\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.36524822695035464,\n \"acc_stderr\": 0.028723863853281285,\n \
\ \"acc_norm\": 0.36524822695035464,\n \"acc_norm_stderr\": 0.028723863853281285\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3533246414602347,\n\
\ \"acc_stderr\": 0.012208408211082428,\n \"acc_norm\": 0.3533246414602347,\n\
\ \"acc_norm_stderr\": 0.012208408211082428\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.030187532060329383,\n\
\ \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.030187532060329383\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.44607843137254904,\n \"acc_stderr\": 0.020109864547181357,\n \
\ \"acc_norm\": 0.44607843137254904,\n \"acc_norm_stderr\": 0.020109864547181357\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5545454545454546,\n\
\ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.5545454545454546,\n\
\ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.4897959183673469,\n \"acc_stderr\": 0.03200255347893782,\n\
\ \"acc_norm\": 0.4897959183673469,\n \"acc_norm_stderr\": 0.03200255347893782\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6567164179104478,\n\
\ \"acc_stderr\": 0.03357379665433431,\n \"acc_norm\": 0.6567164179104478,\n\
\ \"acc_norm_stderr\": 0.03357379665433431\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7076023391812866,\n \"acc_stderr\": 0.03488647713457923,\n\
\ \"acc_norm\": 0.7076023391812866,\n \"acc_norm_stderr\": 0.03488647713457923\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2864137086903305,\n\
\ \"mc1_stderr\": 0.01582614243950236,\n \"mc2\": 0.43403758756918054,\n\
\ \"mc2_stderr\": 0.014103822022452518\n }\n}\n```"
repo_url: https://huggingface.co/dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|arc:challenge|25_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hellaswag|10_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-07T13-33-12.339824.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-07T13-33-12.339824.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-07T13-33-12.339824.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-07T13-33-12.339824.parquet'
- config_name: results
data_files:
- split: 2023_09_07T13_33_12.339824
path:
- results_2023-09-07T13-33-12.339824.parquet
- split: latest
path:
- results_2023-09-07T13-33-12.339824.parquet
---
# Dataset Card for Evaluation run of dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16](https://huggingface.co/dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-07T13:33:12.339824](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16/blob/main/results_2023-09-07T13-33-12.339824.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.45715117494244945,
"acc_stderr": 0.03509698630949401,
"acc_norm": 0.46149986826009826,
"acc_norm_stderr": 0.035082198567284846,
"mc1": 0.2864137086903305,
"mc1_stderr": 0.01582614243950236,
"mc2": 0.43403758756918054,
"mc2_stderr": 0.014103822022452518
},
"harness|arc:challenge|25": {
"acc": 0.48464163822525597,
"acc_stderr": 0.014604496129394908,
"acc_norm": 0.5409556313993175,
"acc_norm_stderr": 0.014562291073601226
},
"harness|hellaswag|10": {
"acc": 0.5871340370444135,
"acc_stderr": 0.0049134290105590705,
"acc_norm": 0.7873929496116312,
"acc_norm_stderr": 0.004083157276012497
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5037037037037037,
"acc_stderr": 0.04319223625811331,
"acc_norm": 0.5037037037037037,
"acc_norm_stderr": 0.04319223625811331
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4605263157894737,
"acc_stderr": 0.04056242252249033,
"acc_norm": 0.4605263157894737,
"acc_norm_stderr": 0.04056242252249033
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.46037735849056605,
"acc_stderr": 0.03067609659938918,
"acc_norm": 0.46037735849056605,
"acc_norm_stderr": 0.03067609659938918
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542126,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542126
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3988439306358382,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.04220773659171453,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.04220773659171453
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3829787234042553,
"acc_stderr": 0.031778212502369216,
"acc_norm": 0.3829787234042553,
"acc_norm_stderr": 0.031778212502369216
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.043036840335373146,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.043036840335373146
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.46206896551724136,
"acc_stderr": 0.041546596717075474,
"acc_norm": 0.46206896551724136,
"acc_norm_stderr": 0.041546596717075474
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.26455026455026454,
"acc_stderr": 0.022717467897708624,
"acc_norm": 0.26455026455026454,
"acc_norm_stderr": 0.022717467897708624
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3412698412698413,
"acc_stderr": 0.042407993275749234,
"acc_norm": 0.3412698412698413,
"acc_norm_stderr": 0.042407993275749234
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
Ramzey/logos | 2023-09-07T13:54:04.000Z | [
"region:us"
] | Ramzey | null | null | null | 0 | 0 | Entry not found |
CreatorPhan/QA_wiki | 2023-09-07T14:02:49.000Z | [
"region:us"
] | CreatorPhan | null | null | null | 0 | 0 | Entry not found |
VMXVMX/alpacatest | 2023-09-07T14:06:08.000Z | [
"region:us"
] | VMXVMX | null | null | null | 0 | 0 | Entry not found |
gunshigupta/traffic_world_demos | 2023-09-07T14:09:07.000Z | [
"license:creativeml-openrail-m",
"region:us"
] | gunshigupta | null | null | null | 0 | 0 | ---
license: creativeml-openrail-m
---
|
Trsa993/Songs-for-transcription | 2023-09-09T11:17:04.000Z | [
"region:us"
] | Trsa993 | null | null | null | 0 | 0 | Entry not found |
ninadn/manor-legal | 2023-09-07T14:21:55.000Z | [
"region:us"
] | ninadn | null | null | null | 0 | 0 | Entry not found |
ruskape/Test | 2023-09-07T14:40:06.000Z | [
"license:openrail",
"region:us"
] | ruskape | null | null | null | 0 | 0 | ---
license: openrail
---
|
syp1229/Young_E | 2023-09-07T14:42:52.000Z | [
"region:us"
] | syp1229 | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sample_rate
dtype: int64
- name: text
dtype: string
- name: scriptId
dtype: int64
- name: fileNm
dtype: string
- name: recrdTime
dtype: float64
- name: recrdQuality
dtype: int64
- name: recrdDt
dtype: string
- name: scriptSetNo
dtype: string
- name: recrdEnvrn
dtype: string
- name: colctUnitCode
dtype: string
- name: cityCode
dtype: string
- name: recrdUnit
dtype: string
- name: convrsThema
dtype: string
- name: gender
dtype: string
- name: recorderId
dtype: string
- name: age
dtype: int64
splits:
- name: train
num_bytes: 4505684820
num_examples: 5400
download_size: 1951573974
dataset_size: 4505684820
---
# Dataset Card for "Young_E"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
QingyiSi/docker_data | 2023-09-07T17:29:24.000Z | [
"region:us"
] | QingyiSi | null | null | null | 0 | 0 | Entry not found |
e-p/activation-datasets | 2023-09-07T15:43:40.000Z | [
"region:us"
] | e-p | null | null | null | 0 | 0 | Entry not found |
DazMashaly/ims | 2023-09-07T15:20:38.000Z | [
"region:us"
] | DazMashaly | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': source
'1': target
splits:
- name: train
num_bytes: 660983817.984
num_examples: 4064
download_size: 712850183
dataset_size: 660983817.984
---
# Dataset Card for "ims"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tony068/Llama2-name-v1 | 2023-09-07T15:21:41.000Z | [
"region:us"
] | Tony068 | null | null | null | 0 | 0 | Entry not found |
Tverous/demo-amrlib | 2023-09-07T15:38:36.000Z | [
"region:us"
] | Tverous | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: uid
dtype: string
- name: article
sequence: string
- name: premise
dtype: string
- name: image
sequence: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: linearized_amr
dtype: string
splits:
- name: train
num_bytes: 47095
num_examples: 6
download_size: 14254
dataset_size: 47095
---
# Dataset Card for "demo-amrlib"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
quocanh34/synthesis_data_v2 | 2023-09-07T15:40:09.000Z | [
"region:us"
] | quocanh34 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: 'null'
- name: sampling_rate
dtype: int64
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1458784152
num_examples: 3078
download_size: 342122736
dataset_size: 1458784152
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "synthesis_data_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anonimoh656r7r65/brug | 2023-09-07T15:42:25.000Z | [
"language:en",
"license:openrail",
"region:us"
] | anonimoh656r7r65 | null | null | null | 0 | 0 | ---
license: openrail
language:
- en
pretty_name: matte
---
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/64f9df86686db7e7d0cd862b/-dXpk-T68-hlUrc7x9AdA.mpga"></audio>
|
HusainMehdi/alpaca-short | 2023-09-07T15:41:28.000Z | [
"region:us"
] | HusainMehdi | null | null | null | 0 | 0 | Entry not found |
HusainMehdi/alpaca-reduced | 2023-09-07T15:42:16.000Z | [
"region:us"
] | HusainMehdi | null | null | null | 0 | 0 | Entry not found |
itllm52/testloi | 2023-09-07T16:20:47.000Z | [
"license:afl-3.0",
"region:us"
] | itllm52 | null | null | null | 0 | 0 | ---
license: afl-3.0
---
|
TenzinGayche/tts_nocs_b1 | 2023-09-07T16:15:25.000Z | [
"region:us"
] | TenzinGayche | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: file_name
dtype:
audio:
sampling_rate: 16000
- name: uni
dtype: string
- name: wylie
dtype: string
- name: url
dtype: string
- name: dept
dtype: string
- name: timestamp
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 5551192766.978
num_examples: 92167
download_size: 5520700816
dataset_size: 5551192766.978
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tts_nocs_b1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
YL95/FXFpMLtext | 2023-09-07T16:16:40.000Z | [
"region:us"
] | YL95 | null | null | null | 0 | 0 | Entry not found |
breadlicker45/test-bread-tokenizer | 2023-09-07T16:48:36.000Z | [
"region:us"
] | breadlicker45 | null | null | null | 0 | 0 | Entry not found |
dtrejopizzo/gsm8k | 2023-09-07T16:41:12.000Z | [
"license:apache-2.0",
"region:us"
] | dtrejopizzo | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
DazMashaly/inpaint_data | 2023-09-07T16:27:42.000Z | [
"region:us"
] | DazMashaly | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 453988831.0
num_examples: 50000
download_size: 0
dataset_size: 453988831.0
---
# Dataset Card for "inpaint_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TenzinGayche/tts_nocs_b2 | 2023-09-07T16:25:19.000Z | [
"region:us"
] | TenzinGayche | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: file_name
dtype:
audio:
sampling_rate: 16000
- name: uni
dtype: string
- name: wylie
dtype: string
- name: url
dtype: string
- name: dept
dtype: string
- name: timestamp
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 5356599043.631
num_examples: 92167
download_size: 5566044725
dataset_size: 5356599043.631
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tts_nocs_b2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
asoria/0907imgs | 2023-09-07T16:32:46.000Z | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:huggan/few-shot-pokemon",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | asoria | null | null | null | 0 | 0 | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'Pokémon BLIP captions'
size_categories:
- n<1K
source_datasets:
- huggan/few-shot-pokemon
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Pokémon BLIP captions
_Dataset used to train [Pokémon text to image model](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning)_
BLIP generated captions for Pokémon images from Few Shot Pokémon dataset introduced by _Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis_ (FastGAN). Original images were obtained from [FastGAN-pytorch](https://github.com/odegeasslbc/FastGAN-pytorch) and captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Examples
> a drawing of a green pokemon with red eyes
> a green and yellow toy with a red nose
> a red and white ball with an angry look on its face
## Citation
If you use this dataset, please cite it as:
```
@misc{pinkney2022pokemon,
author = {Pinkney, Justin N. M.},
title = {Pokemon BLIP captions},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/}}
}
``` |
KingAlpaca/your-dataset-name | 2023-09-07T16:31:28.000Z | [
"region:us"
] | KingAlpaca | null | null | null | 0 | 0 | Entry not found |
sossupummi/openassistant-guanaco | 2023-09-07T16:32:08.000Z | [
"region:us"
] | sossupummi | null | null | null | 0 | 0 | Entry not found |
152334H/tinystories | 2023-09-07T16:38:23.000Z | [
"license:mit",
"region:us"
] | 152334H | null | null | null | 0 | 0 | ---
license: mit
---
|
dtrejopizzo/GSM8K-2023 | 2023-09-07T16:43:47.000Z | [
"license:apache-2.0",
"region:us"
] | dtrejopizzo | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
cspencergo/nft_collection_trades | 2023-09-07T16:56:03.000Z | [
"region:us"
] | cspencergo | null | null | null | 0 | 0 | Entry not found |
TSGallen/ivpaper | 2023-09-07T17:17:57.000Z | [
"region:us"
] | TSGallen | null | null | null | 0 | 0 | Entry not found |
TenzinGayche/tts_nocs_b3 | 2023-09-07T17:20:24.000Z | [
"region:us"
] | TenzinGayche | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: file_name
dtype:
audio:
sampling_rate: 16000
- name: uni
dtype: string
- name: wylie
dtype: string
- name: url
dtype: string
- name: dept
dtype: string
- name: timestamp
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 5624702984.046
num_examples: 92167
download_size: 27280617879
dataset_size: 5624702984.046
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tts_nocs_b3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
saurabhkarn/embedding | 2023-09-07T17:07:01.000Z | [
"license:other",
"region:us"
] | saurabhkarn | null | null | null | 0 | 0 | ---
license: other
---
|
quocanh34/synthesis_data_v3 | 2023-09-07T17:11:24.000Z | [
"region:us"
] | quocanh34 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: 'null'
- name: sampling_rate
dtype: int64
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1458823832
num_examples: 3078
download_size: 342039185
dataset_size: 1458823832
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "synthesis_data_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TenzinGayche/tts_nocs_test | 2023-09-07T17:30:21.000Z | [
"region:us"
] | TenzinGayche | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: file_name
dtype:
audio:
sampling_rate: 16000
- name: uni
dtype: string
- name: wylie
dtype: string
- name: url
dtype: string
- name: dept
dtype: string
- name: timestamp
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 836705600.272
num_examples: 14564
download_size: 2006996986
dataset_size: 836705600.272
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tts_nocs_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
quocanh34/synthesis_data_v4 | 2023-09-07T17:30:01.000Z | [
"region:us"
] | quocanh34 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: 'null'
- name: sampling_rate
dtype: int64
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1459648855
num_examples: 3078
download_size: 344089709
dataset_size: 1459648855
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "synthesis_data_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
adamnukes/rpg | 2023-09-07T17:30:47.000Z | [
"license:openrail",
"region:us"
] | adamnukes | null | null | null | 0 | 0 | ---
license: openrail
---
|
xDAN2099/Flan_Orca_lima_wiz_qa_gsm_share | 2023-09-07T17:32:14.000Z | [
"region:us"
] | xDAN2099 | null | null | null | 0 | 0 | Entry not found |
xDAN2099/notebooks_0908 | 2023-09-07T17:51:00.000Z | [
"region:us"
] | xDAN2099 | null | null | null | 0 | 0 | Entry not found |
indra-inc/docvqa_en_train_valid_2400 | 2023-09-07T17:54:16.000Z | [
"region:us"
] | indra-inc | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: question
dtype: string
- name: docId
dtype: int64
- name: answers
sequence: string
- name: data_split
dtype: string
- name: bounding_boxes
sequence:
sequence: int64
- name: word_list
sequence: string
- name: image_raw
dtype: image
splits:
- name: train
num_bytes: 324047733.0
num_examples: 2000
- name: valid
num_bytes: 207855713.0
num_examples: 400
download_size: 398481109
dataset_size: 531903446.0
---
# Dataset Card for "docvqa_en_train_valid_2400"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
prashanth07/new_data | 2023-09-08T05:21:24.000Z | [
"region:us"
] | prashanth07 | null | null | null | 0 | 0 | Entry not found |
sthibomt/aes-research-dataset | 2023-09-07T18:04:37.000Z | [
"size_categories:10K<n<100K",
"language:en",
"code",
"region:us"
] | sthibomt | null | null | null | 0 | 0 | ---
language:
- en
tags:
- code
pretty_name: asap
size_categories:
- 10K<n<100K
--- |
mesolitica/google-translate-chatalpaca | 2023-09-07T18:12:43.000Z | [
"region:us"
] | mesolitica | null | null | null | 0 | 0 | Entry not found |
jaty54/autotrain-data-yetipy | 2023-09-07T18:33:35.000Z | [
"task_categories:summarization",
"language:en",
"region:us"
] | jaty54 | null | null | null | 0 | 0 | ---
language:
- en
task_categories:
- summarization
---
# AutoTrain Dataset for project: yetipy
## Dataset Description
This dataset has been automatically processed by AutoTrain for project yetipy.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_code": " \n return self.correctwords(originalwords, [newword], **kwargs)",
"target": "def mergewords(self, newword, *originalwords, **kwargs)",
"text": "TODO: Write documentation",
"feat_loss_without_docstring": 9.7212610245,
"feat_loss_with_docstring": 9.146777153,
"feat_factor": 1.0628072448
},
{
"feat_code": " \n\n if size == 0: return [] #for efficiency\n\n context = []\n e = self\n while len(context) < size:\n e = e.previous(True,scope)\n if not e: break\n context.append(e)\n\n if placeholder:\n while len(context) < size:\n context.append(placeholder)\n\n context.reverse()\n return context",
"target": "def leftcontext(self, size, placeholder=None, scope=None)",
"text": "Returns the left context for an element, as a list. This method crosses sentence/paragraph boundaries by default, which can be restricted by setting scope",
"feat_loss_without_docstring": 3.7681286335,
"feat_loss_with_docstring": 3.9110825062,
"feat_factor": 0.9634490266
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_code": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"feat_loss_without_docstring": "Value(dtype='float64', id=None)",
"feat_loss_with_docstring": "Value(dtype='float64', id=None)",
"feat_factor": "Value(dtype='float64', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 80 |
| valid | 20 |
|
anonimoh656r7r65/bru | 2023-09-07T18:34:58.000Z | [
"license:openrail",
"region:us"
] | anonimoh656r7r65 | null | null | null | 0 | 0 | ---
license: openrail
---
|
TenzinGayche/TTS_nocs_pd_b2 | 2023-09-07T18:59:55.000Z | [
"region:us"
] | TenzinGayche | null | null | null | 0 | 0 | Entry not found |
gaclzaq906/CINE_Calidad | 2023-09-07T18:51:08.000Z | [
"region:us"
] | gaclzaq906 | null | null | null | 0 | 0 | Entry not found |
TenzinGayche/TTS_nocs_pd_b1 | 2023-09-07T18:59:03.000Z | [
"region:us"
] | TenzinGayche | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence:
sequence: float32
- name: path
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: speaker_embeddings
sequence: float32
splits:
- name: train
num_bytes: 25886464618.0
num_examples: 92167
download_size: 1609511662
dataset_size: 25886464618.0
---
# Dataset Card for "TTS_nocs_pd_b1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DeCoders/Doctor_mini | 2023-09-07T18:42:38.000Z | [
"license:llama2",
"region:us"
] | DeCoders | null | null | null | 0 | 0 | ---
license: llama2
---
|
TRI-ML/gsm8k-hard | 2023-09-07T18:45:08.000Z | [
"region:us"
] | TRI-ML | null | null | null | 0 | 0 | Entry not found |
ayoungtech/skskstr | 2023-09-12T12:53:50.000Z | [
"region:us"
] | ayoungtech | null | null | null | 0 | 0 | Entry not found |
tnash6/myFirstDataset | 2023-09-07T21:28:32.000Z | [
"license:cc-by-3.0",
"region:us"
] | tnash6 | null | null | null | 0 | 0 | ---
license: cc-by-3.0
---
|
parteeksj/scientific_papers_DANCER | 2023-09-08T13:41:47.000Z | [
"license:mit",
"region:us"
] | parteeksj | null | null | null | 0 | 0 | ---
license: mit
---
|
travisdriver/astrovision-data | 2023-09-22T17:34:45.000Z | [
"region:us"
] | travisdriver | null | null | null | 1 | 0 | ---
pretty_name: AstroVision
viewer: false
---
<a href="https://imgur.com/Vs3Rwin"><img src="https://i.imgur.com/Vs3Rwin.png" title="source: imgur.com" /></a>
---
<a href="https://imgur.com/RjSwdG2"><img src="https://i.imgur.com/RjSwdG2.png" title="source: imgur.com" /></a>
# About
AstroVision is a first-of-a-kind, large-scale dataset of real small body images from both legacy and ongoing deep space missions, which currently features 115,970 densely annotated, real images of sixteen small bodies from eight missions. AstroVision was developed to facilitate the study of computer vision and deep learning for autonomous navigation in the vicinity of a small body, with speicial emphasis on training and evaluation of deep learning-based keypoint detection and feature description methods.
If you find our datasets useful for your research, please cite the [AstroVision paper](https://www.sciencedirect.com/science/article/pii/S0094576523000103):
```bibtex
@article{driver2023astrovision,
title={{AstroVision}: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning},
author={Driver, Travis and Skinner, Katherine and Dor, Mehregan and Tsiotras, Panagiotis},
journal={Acta Astronautica: Special Issue on AI for Space},
year={2023},
volume={210},
pages={393--410}
}
```
Please make sure to like the respository to show support!
# Data format
Following the popular [COLMAP data format](https://colmap.github.io/format.html), each data segment contains the files `images.bin`, `cameras.bin`, and `points3D.bin`, which contain the camera extrinsics and keypoints, camera intrinsics, and 3D point cloud data, respectively.
- `cameras.bin` encodes a dictionary of `camera_id` and [`Camera`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Camera` objects are structured as follows:
- `Camera.id`: defines the unique (and possibly noncontiguious) identifier for the `Camera`.
- `Camera.model`: the camera model. We utilize the "PINHOLE" camera model, as AstroVision contains undistorted images.
- `Camera.width` & `Camera.height`: the width and height of the sensor in pixels.
- `Camera.params`: `List` of cameras parameters (intrinsics). For the "PINHOLE" camera model, `params = [fx, fy, cx, cy]`, where `fx` and `fy` are the focal lengths in $x$ and $y$, respectively, and (`cx`, `cy`) is the principal point of the camera.
- `images.bin` encodes a dictionary of `image_id` and [`Image`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Image` objects are structured as follows:
- `Image.id`: defines the unique (and possibly noncontiguious) identifier for the `Image`.
- `Image.tvec`: $\mathbf{r}^\mathcal{C_ i}_ {\mathrm{BC}_ i}$, i.e., the relative position of the origin of the camera frame $\mathcal{C}_ i$ with respect to the origin of the body-fixed frame $\mathcal{B}$ expressed in the $\mathcal{C}_ i$ frame.
- `Image.qvec`: $\mathbf{q}_ {\mathcal{C}_ i\mathcal{B}}$, i.e., the relative orientation of the camera frame $\mathcal{C}_ i$ with respect to the body-fixed frame $\mathcal{B}$. The user may call `Image.qvec2rotmat()` to get the corresponding rotation matrix $R_ {\mathcal{C}_ i\mathcal{B}}$.
- `Image.camera_id`: the identifer for the camera that was used to capture the image.
- `Image.name`: the name of the corresponding file, e.g., `00000000.png`.
- `Image.xys`: contains all of the keypoints $\mathbf{p}^{(i)} _k$ in image $i$, stored as a ($N$, 2) array. In our case, the keypoints are the forward-projected model vertices.
- `Image.point3D_ids`: stores the `point3D_id` for each keypoint in `Image.xys`, which can be used to fetch the corresponding `point3D` from the `points3D` dictionary.
- `points3D.bin` enocdes a dictionary of `point3D_id` and [`Point3D`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Point3D` objects are structured as follows:
- `Point3D.id`: defines the unique (and possibly noncontiguious) identifier for the `Point3D`.
- `Point3D.xyz`: the 3D-coordinates of the landmark in the body-fixed frame, i.e., $\mathbf{\ell} _{k}^\mathcal{B}$.
- `Point3D.image_ids`: the ID of the images in which the landmark was observed.
- `Point3D.point2D_idxs`: the index in `Image.xys` that corresponds to the landmark observation, i.e., `xy = images[Point3D.image_ids[k]].xys[Point3D.point2D_idxs[k]]` given some index `k`.
These three data containers, along with the ground truth shape model, completely describe the scene.
In addition to the scene geometry, each image is annotated with a landmark map, a depth map, and a visibility mask.
<a href="https://imgur.com/DGUC0ef"><img src="https://i.imgur.com/DGUC0ef.png" title="source: imgur.com" /></a>
- The _landmark map_ provides a consistent, discrete set of reference points for sparse correspondence computation and is derived by forward-projecting vertices from a medium-resolution (i.e., $\sim$ 800k facets) shape model onto the image plane. We classify visible landmarks by tracing rays (via the [Trimesh library](https://trimsh.org/)) from the landmarks toward the camera origin and recording landmarks whose line-of-sight ray does not intersect the 3D model.
- The _depth map_ provides a dense representation of the imaged surface and is computed by backward-projecting rays at each pixel in the image and recording the depth of the intersection between the ray and a high-resolution (i.e., $\sim$ 3.2 million facets) shape model.
- The _visbility mask_ provides an estimate of the non-occluded portions of the imaged surface.
**Note:** Instead of the traditional $z$-depth parametrization used for depth maps, we use the _absolute depth_, similar to the inverse depth parameterization. |
quocanh34/soict_train_synthesis_dataset | 2023-09-07T19:33:17.000Z | [
"region:us"
] | quocanh34 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: sentence_norm
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 4941296103
num_examples: 9807
download_size: 1167222835
dataset_size: 4941296103
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "soict_train_synthesis_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
strumber/dataletsmod | 2023-09-08T02:58:17.000Z | [
"region:us"
] | strumber | null | null | null | 0 | 0 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
tdmckee/Pathomics.io | 2023-09-07T20:05:37.000Z | [
"region:us"
] | tdmckee | null | null | null | 0 | 0 | Entry not found |
vvigl3/daryaaa | 2023-09-07T20:21:13.000Z | [
"license:openrail",
"region:us"
] | vvigl3 | null | null | null | 0 | 0 | ---
license: openrail
---
|
HydraLM/oasst_top1_standardized | 2023-09-07T20:22:12.000Z | [
"region:us"
] | HydraLM | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: message_type
dtype: string
- name: message
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 23156321
num_examples: 37288
download_size: 13298492
dataset_size: 23156321
---
# Dataset Card for "oasst_top1_standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vvigl3/dasha | 2023-09-07T20:31:47.000Z | [
"license:openrail",
"region:us"
] | vvigl3 | null | null | null | 0 | 0 | ---
license: openrail
---
|
lilloukas/MorePlat | 2023-09-13T01:07:09.000Z | [
"license:other",
"region:us"
] | lilloukas | null | null | null | 0 | 0 | ---
license: other
---
|
fluffbufff/fpd_class_obj_lvl | 2023-09-07T21:22:13.000Z | [
"region:us"
] | fluffbufff | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': applique
'1': bag, wallet
'2': bead
'3': belt
'4': bow
'5': buckle
'6': cape
'7': cardigan
'8': coat
'9': collar
'10': dress
'11': epaulette
'12': flower
'13': fringe
'14': glasses
'15': glove
'16': hat
'17': headband, head covering, hair accessory
'18': hood
'19': jacket
'20': jumpsuit
'21': lapel
'22': leg warmer
'23': neckline
'24': pants
'25': pocket
'26': ribbon
'27': rivet
'28': ruffle
'29': scarf
'30': sequin
'31': shirt, blouse
'32': shoe
'33': shorts
'34': skirt
'35': sleeve
'36': sock
'37': sweater
'38': tassel
'39': tie
'40': tights, stockings
'41': top, t-shirt, sweatshirt
'42': umbrella
'43': vest
'44': watch
'45': zipper
splits:
- name: train
num_bytes: 1352082138.17
num_examples: 250918
download_size: 1145006572
dataset_size: 1352082138.17
---
# Dataset Card for "fpd_class_obj_lvl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dtrejopizzo/gsm8k-dataset | 2023-09-07T21:28:33.000Z | [
"license:apache-2.0",
"region:us"
] | dtrejopizzo | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
stasstaf/MS-COCO-validation | 2023-09-08T09:55:24.000Z | [
"region:us"
] | stasstaf | null | null | null | 0 | 0 | Entry not found |
hynky/contract-summ | 2023-09-07T21:58:04.000Z | [
"region:us"
] | hynky | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: instruction
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1052306
num_examples: 749
download_size: 548616
dataset_size: 1052306
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "contract-summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shengqin/bloomz-xss-sqli-2b | 2023-09-07T21:58:31.000Z | [
"region:us"
] | shengqin | null | null | null | 0 | 0 | Entry not found |
hynky/alpacc-contract-summ | 2023-09-10T21:24:02.000Z | [
"region:us"
] | hynky | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 26403302
num_examples: 4560
download_size: 12636847
dataset_size: 26403302
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "alpacc-contract-summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
khalidalt/evol-codealpaca-v1-standardized | 2023-09-07T22:15:07.000Z | [
"region:us"
] | khalidalt | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 252688990
num_examples: 222544
download_size: 131189429
dataset_size: 252688990
---
# Dataset Card for "evol-codealpaca-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/earnings-stocks | 2023-09-07T22:23:09.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: timestamp[ns, tz=EST]
- name: eps_estimate
dtype: float64
- name: reported_eps
dtype: float64
- name: surprise
dtype: float64
splits:
- name: train
num_bytes: 3707357
num_examples: 93309
download_size: 1828938
dataset_size: 3707357
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "earnings-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/earnings-estimate-stocks | 2023-09-07T22:23:19.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: string
- name: current_qtr
dtype: string
- name: no_of_analysts_current_qtr
dtype: int64
- name: next_qtr
dtype: string
- name: no_of_analysts_next_qtr
dtype: int64
- name: current_year
dtype: int64
- name: no_of_analysts_current_year
dtype: int64
- name: next_year
dtype: int64
- name: no_of_analysts_next_year
dtype: int64
- name: avg_estimate_current_qtr
dtype: float64
- name: avg_estimate_next_qtr
dtype: float64
- name: avg_estimate_current_year
dtype: float64
- name: avg_estimate_next_year
dtype: float64
- name: low_estimate_current_qtr
dtype: float64
- name: low_estimate_next_qtr
dtype: float64
- name: low_estimate_current_year
dtype: float64
- name: low_estimate_next_year
dtype: float64
- name: high_estimate_current_qtr
dtype: float64
- name: high_estimate_next_qtr
dtype: float64
- name: high_estimate_current_year
dtype: float64
- name: high_estimate_next_year
dtype: float64
- name: year_ago_eps_current_qtr
dtype: float64
- name: year_ago_eps_next_qtr
dtype: float64
- name: year_ago_eps_current_year
dtype: float64
- name: year_ago_eps_next_year
dtype: float64
splits:
- name: train
num_bytes: 300996
num_examples: 1356
download_size: 123905
dataset_size: 300996
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "earnings-estimate-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/earnings-forecast-stocks | 2023-09-07T22:23:27.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: string
- name: id
dtype: int64
- name: fiscal_end
dtype: string
- name: consensus_eps_forecast
dtype: float64
- name: high_eps_forecast
dtype: float64
- name: low_eps_forecast
dtype: float64
- name: no_of_estimates
dtype: int64
- name: up
dtype: int64
- name: down
dtype: int64
splits:
- name: train
num_bytes: 509571
num_examples: 5699
download_size: 92802
dataset_size: 509571
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "earnings-forecast-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/earnings-surprise-stocks | 2023-09-07T22:23:36.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: string
- name: id
dtype: int64
- name: fiscal_qtr_end
dtype: string
- name: date_reported
dtype: timestamp[ns]
- name: eps
dtype: float64
- name: consensus_forecast
dtype: string
- name: percentage_surprise
dtype: string
splits:
- name: train
num_bytes: 366159
num_examples: 4953
download_size: 91326
dataset_size: 366159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "earnings-surprise-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/eps-revisions-stocks | 2023-09-07T22:23:45.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: string
- name: current_qtr
dtype: string
- name: up_last_7_days_current_qtr
dtype: int64
- name: next_qtr
dtype: string
- name: up_last_7_days_next_qtr
dtype: int64
- name: current_year
dtype: int64
- name: up_last_7_days_current_year
dtype: int64
- name: next_year
dtype: int64
- name: up_last_7_days_next_year
dtype: int64
- name: up_last_30_days_current_qtr
dtype: int64
- name: up_last_30_days_next_qtr
dtype: int64
- name: up_last_30_days_current_year
dtype: int64
- name: up_last_30_days_next_year
dtype: int64
- name: down_last_7_days_current_qtr
dtype: 'null'
- name: down_last_7_days_next_qtr
dtype: 'null'
- name: down_last_7_days_current_year
dtype: 'null'
- name: down_last_7_days_next_year
dtype: 'null'
- name: down_last_30_days_current_qtr
dtype: int64
- name: down_last_30_days_next_qtr
dtype: int64
- name: down_last_30_days_current_year
dtype: int64
- name: down_last_30_days_next_year
dtype: int64
splits:
- name: train
num_bytes: 215572
num_examples: 1356
download_size: 36081
dataset_size: 215572
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eps-revisions-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/eps-trend-stocks | 2023-09-07T22:23:54.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: string
- name: current_qtr
dtype: string
- name: current_estimate_current_qtr
dtype: float64
- name: next_qtr
dtype: string
- name: current_estimate_next_qtr
dtype: float64
- name: current_year
dtype: int64
- name: current_estimate_current_year
dtype: float64
- name: next_year
dtype: int64
- name: current_estimate_next_year
dtype: float64
- name: 7_days_ago_current_qtr
dtype: float64
- name: 7_days_ago_next_qtr
dtype: float64
- name: 7_days_ago_current_year
dtype: float64
- name: 7_days_ago_next_year
dtype: float64
- name: 30_days_ago_current_qtr
dtype: float64
- name: 30_days_ago_next_qtr
dtype: float64
- name: 30_days_ago_current_year
dtype: float64
- name: 30_days_ago_next_year
dtype: float64
- name: 60_days_ago_current_qtr
dtype: float64
- name: 60_days_ago_next_qtr
dtype: float64
- name: 60_days_ago_current_year
dtype: float64
- name: 60_days_ago_next_year
dtype: float64
- name: 90_days_ago_current_qtr
dtype: float64
- name: 90_days_ago_next_qtr
dtype: float64
- name: 90_days_ago_current_year
dtype: float64
- name: 90_days_ago_next_year
dtype: float64
splits:
- name: train
num_bytes: 300316
num_examples: 1356
download_size: 140021
dataset_size: 300316
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eps-trend-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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