Duplicate from HuggingFaceH4/ultrachat_200k

Co-authored-by: Edward Beeching <edbeeching@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- en
+license: mit
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+pretty_name: UltraChat 200k
+configs:
+- config_name: default
+  data_files:
+  - split: train_sft
+    path: data/train_sft-*
+  - split: test_sft
+    path: data/test_sft-*
+  - split: train_gen
+    path: data/train_gen-*
+  - split: test_gen
+    path: data/test_gen-*
+dataset_info:
+  features:
+  - name: prompt
+    dtype: string
+  - name: prompt_id
+    dtype: string
+  - name: messages
+    list:
+    - name: content
+      dtype: string
+    - name: role
+      dtype: string
+  splits:
+  - name: train_sft
+    num_bytes: 1397058554
+    num_examples: 207865
+  - name: test_sft
+    num_bytes: 154695659
+    num_examples: 23110
+  - name: train_gen
+    num_bytes: 1347396812
+    num_examples: 256032
+  - name: test_gen
+    num_bytes: 148276089
+    num_examples: 28304
+  download_size: 1624049723
+  dataset_size: 3047427114
+---
+
+# Dataset Card for UltraChat 200k
+
+## Dataset Description
+
+This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model.
+
+The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic:
+
+- Selection of a subset of data for faster supervised fine tuning.
+- Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?"
+- Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either.
+
+## Dataset Structure
+
+The dataset has four splits, suitable for:
+
+* Supervised fine-tuning (`sft`).
+* Generation ranking (`gen`) via techniques like rejection sampling or PPO.
+
+The number of examples per split is shown as follows:
+
+
+|  train_sft | test_sft  | train_gen | test_gen |
+|:-------:|:-----------:|:-----:| :-----:|
+|  207865 |       23110 | 256032 | 28304 |
+
+The dataset is stored in parquet format with each entry using the following schema:
+```
+
+{
+    "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...",
+    "messages":[
+        {
+            "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...",
+            "role": "user"
+        },
+        {
+            "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...",
+            "role": "assistant"
+        },
+        {
+            "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details.  ...",
+            "role": "user"
+        },
+        {
+            "content": "Certainly! ....",
+            "role": "assistant"
+        },
+        {
+            "content": "That's really interesting! I would love to hear more...",
+            "role": "user"
+        }
+        {
+            "content": "Certainly! ....",
+            "role": "assistant"
+        },
+    ],
+    "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af"
+}
+```
+
+## Citation
+
+If you find this dataset is useful in your work, please cite the original UltraChat dataset:
+
+```
+@misc{ding2023enhancing,
+      title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, 
+      author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
+      year={2023},
+      eprint={2305.14233},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

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