initial commit

README.md

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----
-license: apache-2.0
----
+---
+language:
+  - en
+license: apache-2.0
+tags:
+  - text
+pretty_name: Reddit DadJokes dataset
+size_categories:
+  - "100K<n<1M"
+task_categories:
+  - text-generation
+dataset_info:
+  config_name: default
+  features:
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: instruction
+    dtype: string
+  splits:
+    - name: train
+      num_bytes: 2734101179
+      num_examples: 147753
+train-eval-index:
+  - config: default
+    task: text-generation
+    splits:
+      train_split: train
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "data/train/*"
+---
+
+# Reddit /r/DadJokes dataset
+
+Dataset is based on a [semi-public Pushshift Reddit dataset](https://convokit.cornell.edu/documentation/subreddit.html).
+
+## Source data
+
+The actual extract of all top-level posts on `/r/DadJokes` can be found in the `src/reddit_dadjokes.csv.gz` file. The raw post contents are very noisy, so we did a couple of steps to clean things up:
+
+* Used a `Llama-8B-instruct` to split each dad joke into the `intro` and a `punchline` parts. See `src/parse.py` script for details.
+* Removed too short and too long posts. Also removed all punchlines being emojis or URLs.
+* There are a lot of duplicate submissions present, so we used the `intfloat/e5-base-v2` model for semantic deduplication.
+
+## Stats
+
+147753 jokes split to `intro` and `punchline` parts. Jokes are from 2014 till 2022. 
+
+## Examples
+
+To be Alpaca-format compatible we use the `Continue a dad joke:` instruction by default, but it's up to you for using something else.
+
+
+```json
+{"input": "A grizzly kept talking to me", "output": "He was unbearable", "instruction": "Continue a dad joke:"}
+{"input": "I rubbed mayonnaise on my eyes", "output": "Oh fuck oh shit it hurts please help this is no joke it pains fuck help me already", "instruction": "Continue a dad joke:"}
+{"input": "What do you say to encourage an asteroid?", "output": "Go little rockstar.", "instruction": "Continue a dad joke:"}
+{"input": "They always ask me why my mood is always negative", "output": "Just multiply by -1 \ud83d\ude09", "instruction": "Continue a dad joke:"}
+{"input": "Recently I started working with horses", "output": "It's a stable job.", "instruction": "Continue a dad joke:"}
+{"input": "My favorite word is \"drool\"", "output": "Just rolls off the tongue.", "instruction": "Continue a dad joke:"}
+{"input": "Where\u2019s a dogs favorite place to eat", "output": "At Woofle House", "instruction": "Continue a dad joke:"}
+{"input": "Why don't oysters share their pearls?", "output": "They're shellfish \ud83d\udc1a\ud83d\ude02!", "instruction": "Continue a dad joke:"}
+{"input": "What do you call two guys hanging out above a window?", "output": "Kurt & Rod", "instruction": "Continue a dad joke:"}
+{"input": "Me: I'm not saying a word without my lawyer present!", "output": "So where's my present?!", "instruction": "Continue a dad joke:"}
+```
+
+## License
+
+Apache 2.0

data/train/train.jsonl.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f650a8375a414240f7c76c2adf276561bc9e17ad3aab02f720796242b440678c
+size 6901495

src/convert.py

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+from argparse import ArgumentParser
+import json
+from tqdm import tqdm
+from dataclasses import dataclass
+from typing import List, Optional, Dict, Set
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import hnswlib
+
+@dataclass
+class Doc:
+    input: str
+    output: str
+    
+    @staticmethod
+    def from_json(doc: Dict):
+        return Doc(input=doc['input'], output=doc['output'])
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(prog="convert.py", description="dadjokes reddit CSV parser")
+    parser.add_argument("--data", action="store", help="path to input JSON file", required=True)
+    parser.add_argument("--out", action="store", help="path to output file", required=True)
+    parser.add_argument("--inst", action="store", help="alpaca instruction", required=True)
+
+    args = parser.parse_args()
+    print(args)
+
+    model = SentenceTransformer("intfloat/e5-base-v2",device="cuda")
+
+    with open(args.data, 'r') as input:
+        docs: List[Doc] = []
+        for line in tqdm(input.readlines()):
+            item = Doc.from_json(json.loads(line))
+            docs.append(item)
+        embeddings = model.encode([f"passage: {doc.input} {doc.output}" for doc in docs], batch_size=512, show_progress_bar=True)
+        p = hnswlib.Index(space = 'cosine', dim = 768)
+        print("building index")
+        p.init_index(max_elements = len(docs), ef_construction = 200, M = 16)
+        p.add_items(embeddings, [id for id, doc in enumerate(docs)])
+        print("computing similarity")
+        labels, distances = p.knn_query(embeddings, k = 10)
+        skips: Set[int] = set()
+        print("search done, exporting")
+        dupe_count = 0
+        broken_count = 0
+        with open(args.out,'w') as output:
+            for (index, doc), label_list, dist_list in zip(enumerate(docs), labels.tolist(), distances.tolist()):
+                if index not in skips:
+                    if "http" not in doc.output:
+                        jdoc = {"input": doc.input, "output": doc.output, "instruction": args.inst}
+                        output.write(json.dumps(jdoc) + '\n')
+                    else:
+                        broken_count += 1
+                else:
+                    dupe_count += 1
+                skips.add(index)
+                for label, dist in zip(label_list, dist_list):
+                    if (dist < 0.07):
+                        skips.add(label)
+
+        print(f"done: dupes={dupe_count} broken={broken_count}")

src/parse.py

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+from transformers import (
+    pipeline,
+)
+import torch
+import json
+from transformers.pipelines.pt_utils import KeyDataset
+from tqdm import tqdm
+from datasets import Dataset
+from argparse import ArgumentParser
+from typing import Dict, List
+
+# Reddit CSV dump parser script
+
+def make_prompt_mistral(data: Dict[str, List]) -> Dict[str, List]:
+    prompt_template = "### Instruction:\n{instruct}:\n\n### Input:\n{input}\n\n### Response:\n"
+    result = []
+    for doc in data["joke"]:
+        prompt = prompt_template.format(
+            instruct="For the following joke, add a | separator between intro part and the punchline. "
+            "Do not change the sentence, only add a separator. "
+            "Full sencence should be considered a punchline. "
+            "A question is a full intro section, everything following a question must be considered punchline. "
+            "Do not repeat the punchline, do not change words in the sentence.",
+            input=doc,
+        )
+        result.append(prompt)
+    return {"prompt": result}
+
+
+def make_prompt_llama3(data: Dict[str, List]) -> Dict[str, List]:
+    prompt_template = "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{instruct}<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{input}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
+    result = []
+    for doc in data["joke"]:
+        prompt = prompt_template.format(
+            instruct="For the following joke, add a | separator between intro part and the punchline. "
+            "Do not change the sentence, only add a separator. "
+            "Full sencence should be considered a punchline. "
+            "A question is a full intro section, everything following a question must be considered punchline. "
+            "Do not repeat the punchline, do not change words in the sentence. "
+            "Do not repeat this instruction, only output the result."
+            "Do not tell you're an assistant.",
+            input=doc[:256],
+        )
+        result.append(prompt)
+    return {"prompt": result}
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(prog="batch_split", description="dadjokes")
+    parser.add_argument("--data", action="store", help="path to reddit.csv", required=True)
+    parser.add_argument("--out", action="store", help="path to out file", required=True)
+    args = parser.parse_args()
+    print(args)
+
+    dataset = Dataset.from_csv(args.data, split="train")
+    generator = pipeline(
+        task="text-generation",
+        # model="mistralai/Mistral-7B-Instruct-v0.3",
+        model="meta-llama/Meta-Llama-3-8B-Instruct",
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+    generator.tokenizer.pad_token_id = 128009
+
+    prompts = KeyDataset(dataset.map(function=make_prompt_llama3, batched=True), "prompt")
+    with open(args.out, "w") as f:
+        for result in tqdm(
+            generator(
+                prompts,
+                return_full_text=False,
+                max_new_tokens=128,
+                num_return_sequences=1,
+                batch_size=24,
+            ),
+            total=len(prompts),
+        ):
+            raw_text = result[0]["generated_text"].split("\n")
+            joke = raw_text[-1]
+            if "|" in joke:
+                tokens = joke.split("|")
+                if len(tokens) == 2:
+                    intro = tokens[0].strip()
+                    punch = tokens[1].strip()
+                    if len(intro) > 10 and len(punch) > 5:
+                        f.write(json.dumps({"input": intro, "output": punch}) + "\n")

src/reddit_dadjokes.csv.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fb55e62dc7077144a5b82165109b8770f94da60eee701045f39c7a7a28c7083a
+size 19162024

src/requirements.txt

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+transformers
+tqdm
+datasets
+torch
+sentence_transformers
+hnswlib