Create README.md

README.md

@@ -1,103 +1,30 @@
 ---
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: meta.hexsha
-    dtype: string
-  - name: meta.size
-    dtype: int64
-  - name: meta.ext
-    dtype: string
-  - name: meta.lang
-    dtype: string
-  - name: meta.max_stars_repo_path
-    dtype: string
-  - name: meta.max_stars_repo_name
-    dtype: string
-  - name: meta.max_stars_repo_head_hexsha
-    dtype: string
-  - name: meta.max_stars_repo_licenses
-    sequence: string
-  - name: meta.max_stars_count
-    dtype: int64
-  - name: meta.max_stars_repo_stars_event_min_datetime
-    dtype: string
-  - name: meta.max_stars_repo_stars_event_max_datetime
-    dtype: string
-  - name: meta.max_issues_repo_path
-    dtype: string
-  - name: meta.max_issues_repo_name
-    dtype: string
-  - name: meta.max_issues_repo_head_hexsha
-    dtype: string
-  - name: meta.max_issues_repo_licenses
-    sequence: string
-  - name: meta.max_issues_count
-    dtype: int64
-  - name: meta.max_issues_repo_issues_event_min_datetime
-    dtype: string
-  - name: meta.max_issues_repo_issues_event_max_datetime
-    dtype: string
-  - name: meta.max_forks_repo_path
-    dtype: string
-  - name: meta.max_forks_repo_name
-    dtype: string
-  - name: meta.max_forks_repo_head_hexsha
-    dtype: string
-  - name: meta.max_forks_repo_licenses
-    sequence: string
-  - name: meta.max_forks_count
-    dtype: int64
-  - name: meta.max_forks_repo_forks_event_min_datetime
-    dtype: string
-  - name: meta.max_forks_repo_forks_event_max_datetime
-    dtype: string
-  - name: meta.avg_line_length
-    dtype: float64
-  - name: meta.max_line_length
-    dtype: int64
-  - name: meta.alphanum_fraction
-    dtype: float64
-  - name: meta.converted
-    dtype: bool
-  - name: meta.num_tokens
-    dtype: int64
-  - name: meta.lm_name
-    dtype: string
-  - name: meta.lm_label
-    dtype: string
-  - name: meta.lm_q1_score
-    dtype: float64
-  - name: meta.lm_q2_score
-    dtype: float64
-  - name: meta.lm_q1q2_score
-    dtype: float64
-  - name: text_lang
-    dtype: string
-  - name: text_lang_conf
-    dtype: float64
-  - name: label
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 259675872.10744715
-    num_examples: 14459
-  - name: validation
-    num_bytes: 32452749.214894325
-    num_examples: 1807
-  - name: test
-    num_bytes: 32470708.677658513
-    num_examples: 1808
-  download_size: 142960556
-  dataset_size: 324599329.99999994
+language:
+- en
+pretty_name: Math Notebooks
+size_categories:
+- 10K<n<100K
 ---
+# Math Notebooks
+This repository contains mathematically informative ipython notebooks that were collated from OpenWebMath, RedPajama, and the Algebraic Stack in the [AutoMathText](https://huggingface.co/datasets/math-ai/AutoMathText) effort. Zhang et. al. used Qwen 72B to score text with the following prompt:
+```
+<system>
+You are ChatGPT, equipped with extensive expertise in mathematics and coding, and skilled
+in complex reasoning and problem-solving. In the following task, I will present a text excerpt
+from a website. Your role is to evaluate whether this text exhibits mathematical intelligence
+and if it is suitable for educational purposes in mathematics. Please respond with only YES
+or NO
+</system>
+  User: {
+    “url”: “{url}”,
+    “text”: “{text}”
+}
+1. Does the text exhibit elements of mathematical intelligence? Respond with YES or NO
+2. Is the text suitable for educational purposes for YOURSELF in the field of mathematics? Respond with YES or NO
+```
+
+The responses to these questions were each scored with the function:
+$$LM–Score(\cdot) = \frac{exp(logit('YES'))}{exp(logit('YES')) + exp(logit('NO'))}$$
+
+These scores are found in the `meta.lm_q1_score` and `meta.lm_q2_score` columns. A total score (`meta.lm_q1q2_score`) is achieved by taking the product of the two scores.
+$$ LM–Score(Q_1, Q_2) = LM–Score(Q_1) \cdot LM–Score(Q_2) $$