|
--- |
|
configs: |
|
- config_name: all |
|
data_files: |
|
- path: |
|
- all.jsonl.zst |
|
split: train |
|
- config_name: sample_k100 |
|
data_files: |
|
- path: |
|
- sample_k100.jsonl.zst |
|
split: train |
|
- config_name: sample_k1000 |
|
data_files: |
|
- path: |
|
- sample_k1000.jsonl.zst |
|
split: train |
|
- config_name: sample_k10000 |
|
data_files: |
|
- path: |
|
- sample_k10000.jsonl.zst |
|
split: train |
|
- config_name: sample_k100000 |
|
data_files: |
|
- path: |
|
- sample_k100000.jsonl.zst |
|
split: train |
|
- config_name: sample_k200 |
|
data_files: |
|
- path: |
|
- sample_k200.jsonl.zst |
|
split: train |
|
- config_name: sample_k2000 |
|
data_files: |
|
- path: |
|
- sample_k2000.jsonl.zst |
|
split: train |
|
- config_name: sample_k20000 |
|
data_files: |
|
- path: |
|
- sample_k20000.jsonl.zst |
|
split: train |
|
- config_name: sample_k200000 |
|
data_files: |
|
- path: |
|
- sample_k200000.jsonl.zst |
|
split: train |
|
- config_name: sample_k500 |
|
data_files: |
|
- path: |
|
- sample_k500.jsonl.zst |
|
split: train |
|
- config_name: sample_k5000 |
|
data_files: |
|
- path: |
|
- sample_k5000.jsonl.zst |
|
split: train |
|
- config_name: sample_k50000 |
|
data_files: |
|
- path: |
|
- sample_k50000.jsonl.zst |
|
split: train |
|
license: odc-by |
|
task_categories: |
|
- text-generation |
|
- text-classification |
|
language: |
|
- en |
|
tags: |
|
- pretraining |
|
- language modelling |
|
--- |
|
# High Quality Text Dataset |
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|
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A curated collection of English-language texts for AI training and research. |
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|
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### Sources |
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- [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) |
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- [openbmb/Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) |
|
- [Zyphra/Zyda-2](https://huggingface.co/datasets/Zyphra/Zyda-2) |
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|
|
Each dataset was processed as follows: |
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|
|
1. Split into approximately 2 000-token chunks using the [LLaMA 3.1 tokenizer](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). |
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2. Cleaned by normalizing spaces, punctuation, and characters, and replacing emails and phone numbers with placeholders. |
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3. Scored using the [`agentlans/GIST-all-MiniLM-L6-v2-quality-v3`](https://huggingface.co/agentlans/GIST-all-MiniLM-L6-v2-quality-v3) classifier: |
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- Only chunks with a quality score greater than 1 were included. |
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4. Removed exact duplicates. |
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|
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After filtering, 100 000 chunks per source were included in the final dataset. |
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|
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### Clustering |
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Agglomerative clustering was applied using embeddings from the [`Snowflake/snowflake-arctic-embed-xs`](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) |
|
model at multiple cluster counts: 100, 200, 500, 1 000, 2 000, 5 000, 10 000, 20 000, 50 000, 100 000, and 200 000 clusters, enabling flexible dataset configurations. |
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|
|
### Example Entry |
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|
|
```json |
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{ |
|
"text": "Dr. Louise Glew has been appointed the Global Lead Scientist for WWF's Global Science Team. Louise's research focuses on understanding the social and ecological impacts of conservation interventions [...]", |
|
"quality": 2.0699, |
|
"source": "openbmb/Ultra-FineWeb" |
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} |
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``` |
|
|
|
### Limitations |
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|
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- Primarily focuses on academic, educational, and pedagogical content intended for a general audience. |
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- May include outdated, unreliable, or controversial information (such as self-published material, pseudoscience, or conspiracy theories). |
|
- Quality scores evaluate syntax and tone, but do not guarantee factual accuracy. |
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- Occasional repetition may occur (for example, dictionary entries or geographic distance calculations). |
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- Entries might be interrupted mid-word or mid-sentence. |
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|
|
### Licence |
|
Provided under the Open Data Commons Attribution License (ODC-BY). |