Update README.md

README.md

@@ -8,8 +8,8 @@ inference: false
 ---
 # llm-data-textbook-quality-fasttext-classifer-v2
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/60e50ce5350d181892d5a636/pxajU51PTfF9qpDiugh4n.png)
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60e50ce5350d181892d5a636/IPmnl6Fc4bvUYnpkVZg8N.png)
 
 ## **"Garbage in, garbage out. A language model is only as good as its training data irrespective of its parameter count."**
 
@@ -122,16 +122,20 @@ The score can be roughly interpreted as:
 |[HuggingFaceTB/cosmopedia auto_math_text](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) |First 100,000 | 1.347 |Synthetic|
 |[armanc/scientific_papers pubmed](https://huggingface.co/datasets/armanc/scientific_papers) |First 100,000 | 1.260 |Real|
 |[HuggingFaceTB/cosmopedia stories](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) |First 100,000 | 1.154 |Synthetic|
+|[timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) |First 100,000 | 1.115 |Real|
 |[open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math) |First 100,000 | 1.089 |Real|
 |[armanc/scientific_papers arxiv](https://huggingface.co/datasets/armanc/scientific_papers) |First 100,000 | 1.068 |Real|
 |[HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)| First 100,000 | 1.056 |Real|
 |[NousResearch/dolma-v1_7-305B*](https://huggingface.co/datasets/NousResearch/dolma-v1_7-305B) |First 100,000 | 1.037 |Real|
+|[tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) |First 100,000 | 1.020 |Synthetic|
 |[BEE-spoke-data/fineweb-100k_en-med](https://huggingface.co/datasets/BEE-spoke-data/fineweb-100k_en-med)| First 100,000 | 1.019 |Real|
 |[JeanKaddour/minipile](https://huggingface.co/datasets/JeanKaddour/minipile)| First 100,000 | 0.998 |Real|
 |[togethercomputer/RedPajama-Data-V2 en 2023-06](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)| First 100,000 | 0.985|Real|
 |[wikipedia en 20220301](https://huggingface.co/datasets/wikipedia) |First 100,000 | 0.975 |Real|
+|[Replete-AI/code_bagel](https://huggingface.co/datasets/Replete-AI/code_bagel)| First 100,000 | 0.950 |Synthetic|
 |[allenai/c4 en](https://huggingface.co/datasets/allenai/c4)| First 100,000| 0.934 |Real|
 |[mattymchen/refinedweb-3m](https://huggingface.co/datasets/mattymchen/refinedweb-3m)| First 100,000 | 0.857 |Real|
+|[iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)| First 100,000 | 0.849 |Synthetic|
 |[tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)| First 100,000 | 0.835 |Real|
 |[BEE-spoke-data/FineMeme-100k](https://huggingface.co/datasets/BEE-spoke-data/FineMeme-100k)| First 100,000 | 0.716 |Real|
 |[neuralcatcher/hateful_memes](https://huggingface.co/datasets/neuralcatcher/hateful_memes)| First 100,000 | 0.070 |Real|
@@ -144,6 +148,7 @@ The classifier aligns with the expectation.
 - In general, the later a dataset is released, the higher the educational value it is because of increasing focus on data quality in the research community.
 - Textbook category (mostly synethetic) scores the highest, becuase they are created for educational value, reflecting the effectiveness of this model.   
 - Maths/ paper category scores the second highest, because of its density of knowledge.
+- Instruction dataset scores less than textbook because depth of knowledge in conversation is usually less dense in textbook, but they are in general more educative than unfiltered web.
 - Wikipedia scores comparatively lower because it also contains information (e.g. result of a match, award of a movie star) that has smaller educational value.  
 - Web scores low (if no filtering is applied) because it contains all domains.
 - Meme scores the lowest as expected. Hateful memes almost got zero point.