# Dataset Card for "Clean-Instruct"

[yahma/alpaca-cleaned](https://hf.co/datasets/yahma/alpaca-cleaned) + [crumb/gpt4all-clean](https://hf.co/datasets/crumb/gpt4all-clean) + GPTeacher-Instruct-Dedup

It isn't perfect, but it's 443k high quality semi-cleaned instructions without "As an Ai language model".

```python
from datasets import load_dataset
dataset = load_dataset("crumb/clean-instruct", split="train")
def promptify(example):
  if example['input']!='':
    return {"text": f"<instruction> {example['instruction']} <input> {example['input']} <output> {example['output']}"}
  return {"text": f"<instruction> {example['instruction']} <output> {example['output']}"}
dataset = dataset.map(promptify, batched=False)
dataset = dataset.remove_columns(["instruction", "input", "output"])
```
task_categories:conversational language:en license:mit region:us
{"language": ["en"], "license": "mit", "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 650842125.0, "num_examples": 443612}], "download_size": 357775511, "dataset_size": 650842125.0}}
TAGS
#task_categories-conversational #language-English #license-mit #region-us 

# Dataset Card for "Clean-Instruct"

yahma/alpaca-cleaned + crumb/gpt4all-clean + GPTeacher-Instruct-Dedup

It isn't perfect, but it's 443k high quality semi-cleaned instructions without "As an Ai language model".


# Dataset Card for "Clean-Instruct"

yahma/alpaca-cleaned + crumb/gpt4all-clean + GPTeacher-Instruct-Dedup

It isn't perfect, but it's 443k high quality semi-cleaned instructions without "As an Ai language model".
TAGS
#task_categories-conversational #language-English #license-mit #region-us 
 # Dataset Card for "Clean-Instruct"

yahma/alpaca-cleaned + crumb/gpt4all-clean + GPTeacher-Instruct-Dedup

It isn't perfect, but it's 443k high quality semi-cleaned instructions without "As an Ai language model".