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README.md

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+---
+language:
+- en
+---
+
+# Curated Manifold Markets Subset
+
+There has been substantial interest in using large language models to answer
+forecasting competition questions like the ones found on [Metaculus](https://www.metaculus.com/home/)
+or [Manifold Markets](https://manifold.markets/). Metaculus's API is restricted
+to teams that ask for permission to use it, but Manifold's API is openly available
+under very liberal terms. This makes Manifold an appealing option for forecasting
+model authors but for one problem: Manifold takes a libertarian approach to question
+moderation and allows a lot of junk markets on the platform. While this makes it
+an excellent incubator for new question formats and ideas, it can make training
+models based on the resulting data a little tricky. This dataset is the top 10,000
+resolved Manifold Markets questions in yes/no format as graded by the criteria
+set out in the Limitations and Biases section below using an LLM evaluator. The
+result is a much higher signal dataset than what you would get by pulling from
+the API with minimal filtering.
+
+## Usage
+
+## Use Cases
+
+- Baseline tuning strategy and validation set for answering forecasting questions
+- Because forecasting questions are resolved yes/no they can be used to train weave
+evaluator
+- Good foundation to backtranslate from to make further datasets
+
+### Quickstart With HuggingFace Datasets
+
+```
+import datasets
+import datetime
+
+def format_market_details(market):
+    question = market.get("question")
+    yes_probability = market.get("probability") * 100
+    no_probability = (1 - market.get("probability")) * 100
+    unique_bettor_count = market.get("uniqueBettorCount")
+    creator_name = market.get("creatorName")
+    created_time = datetime.datetime.fromtimestamp(market.get("createdTime") / 1000).strftime("%Y-%m-%d at %H:%M UTC")
+    close_time = datetime.datetime.fromtimestamp(market.get("closeTime") / 1000).strftime("%Y-%m-%d at %H:%M UTC")
+    text_description = market.get("textDescription")
+    resolution = market.get("resolution").title() + "."
+    out = ""
+    out += "Manifold Markets\n\n"
+    out += f"{question}\n"
+    out += f"YES {yes_probability:.2f}% NO {no_probability:.2f}% "
+    out += f"| {unique_bettor_count} Bettors\n"
+    out += f"Creator: {creator_name}\n"
+    out += f"Created: {created_time}\n"
+    out += f"Closes: {close_time}\n\n"
+    out += f"Description & Resolution Criteria: {text_description}\n\n"
+    out += f"Resolution: {resolution}"
+    return out
+
+train = datasets.load_dataset("jdpressman/retro-weave-eval-jdp-v0.1")["train"]
+
+for market_details in train:
+    print(format_market_details(market_details))
+```
+
+### Raw Quickstart
+
+```
+import json
+import datetime
+
+def format_market_details(market):
+    question = market.get("question")
+    yes_probability = market.get("probability") * 100
+    no_probability = (1 - market.get("probability")) * 100
+    unique_bettor_count = market.get("uniqueBettorCount")
+    creator_name = market.get("creatorName")
+    created_time = datetime.datetime.fromtimestamp(market.get("createdTime") / 1000).strftime("%Y-%m-%d at %H:%M UTC")
+    close_time = datetime.datetime.fromtimestamp(market.get("closeTime") / 1000).strftime("%Y-%m-%d at %H:%M UTC")
+    text_description = market.get("textDescription")
+    resolution = market.get("resolution").title() + "."
+    out = ""
+    out += "Manifold Markets\n\n"
+    out += f"{question}\n"
+    out += f"YES {yes_probability:.2f}% NO {no_probability:.2f}% "
+    out += f"| {unique_bettor_count} Bettors\n"
+    out += f"Creator: {creator_name}\n"
+    out += f"Created: {created_time}\n"
+    out += f"Closes: {close_time}\n\n"
+    out += f"Description & Resolution Criteria: {text_description}\n\n"
+    out += f"Resolution: {resolution}"
+    return out
+
+with open("train.json") as infile:
+    train = json.load(infile)
+
+for market_details in train:
+    print(format_market_details(market_details))
+```
+
+## License
+
+While no explicit license is given for this dataset, the Manifold Markets API page
+informs the user they should "Feel free to use the API for any purpose you'd like."
+and provides a site dump as a convenience. This implies that the Manifold team should
+be okay with this dataset. If they're not they can contact me or HuggingFace to
+have it taken down.
+
+## Data Structure
+
+The data structure is a list of Manifold Market Details JSON objects as they're
+given by the API. Here is a sample item:
+
+> {"id": "JOLqUM7VZVWGyPMyjgOM", "creatorId": "fP5OQUWYt4MW17A2giGjMGsw1uu2", "creatorUsername": "LarsDoucet", "creatorName": "Lars Doucet", "createdTime": 1640805909009, "creatorAvatarUrl": "https://lh3.googleusercontent.com/a-/AOh14Gh_23ZmfLBMGBR2crNwb0T8hBnPAap5nkWiSKuB=s96-c", "closeTime": 1672531200000, "question": "Will Joe Rogan interview a guest about Georgism in 2022?", "slug": "will-joe-rogan-interview-a-guest-ab", "url": "https://manifold.markets/LarsDoucet/will-joe-rogan-interview-a-guest-ab", "pool": {"NO": 103.73708237350644, "YES": 996.054209916458}, "probability": 0.031616466242030815, "p": 0.23866581093751968, "totalLiquidity": 184.67960075647989, "outcomeType": "BINARY", "mechanism": "cpmm-1", "volume": 4123.3286725950675, "volume24Hours": 0, "isResolved": true, "resolution": "NO", "resolutionTime": 1672976192735, "resolutionProbability": 0.03, "uniqueBettorCount": 50, "lastUpdatedTime": 1672976168074, "lastBetTime": 1672069861903, "lastCommentTime": 1672976161444, "description": "This market will resolve to \"Yes\" if, by December 31, 11:59:59 PM CT, Joseph James Rogan (aka \"Joe Rogan\"), host of the \"Joe Rogan Experience\" on Spotify, invites a guest onto that podcast who mentions any of these three words -- \"Georgism\", \"Geoism\", or \"Land Value Tax\" -- in a favorable context.\n#JoeRogan\n#Georgism\n#Economics\n#Podcast", "groupSlugs": ["georgism", "politics-default", "economics-default"], "textDescription": "This market will resolve to \"Yes\" if, by December 31, 11:59:59 PM CT, Joseph James Rogan (aka \"Joe Rogan\"), host of the \"Joe Rogan Experience\" on Spotify, invites a guest onto that podcast who mentions any of these three words -- \"Georgism\", \"Geoism\", or \"Land Value Tax\" -- in a favorable context.\n#JoeRogan\n#Georgism\n#Economics\n#Podcast"}
+
+## Biases and Limitations
+
+The curation was performed by [SOLAR 10.7B base](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
+using the [weave evaluator](https://github.com/JD-P/RetroInstruct/). Three rubrics
+were used to filter out undesirable traits in a market:
+
+- [The extent to which the market is about the personal life of a non-famous person](https://github.com/JD-P/RetroInstruct/blob/main/ManifoldSteelmanning/personal_rubric.txt)
+
+- [Whether the market disregards the established rules and best practices for
+drafting forecasting questions](https://github.com/JD-P/RetroInstruct/blob/main/ManifoldSteelmanning/resolvable_rubric.txt)
+
+- [How meta, luck-based, or facetious a market is](https://github.com/JD-P/RetroInstruct/blob/main/ManifoldSteelmanning/degeneracy_rubric.txt)
+
+Because all the questions in this rubric are answered with "yes" the evaluator
+could be biased towards texts with "no" nature that make the evaluator answer
+no more frequently. I did a quick spot check that the distribution of yes and
+no resolutions on forecasting questions chosen didn't look very skewed, but it
+might be a good idea to get the distribution of yes and no resolutions in the
+dataset as a whole versus the subset I chose with weave evaluator. I will do
+this later.
+
+## Planned Improvements
+
+- Train models on this dataset to get a forecasting baseline
+- Check distribution of yes and no questions in chosen subset vs. the distribution
+on the full dataset
+- Change weave evaluator questions to have a mix of yes and no answers desired

train.json

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