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README.md

@@ -1,4 +1,7 @@
 ---
+license: mit
+language:
+- en
 dataset_info:
   features:
     - name: text
@@ -13,11 +16,16 @@ dataset_info:
       dtype: float64
     - name: legibility
       dtype: float64
+tags:
+- pre-1900
+- historical
+- physics
+- nlp
 ---
 
 # Pre-1900 Corpus
 
-Cleaned corpus of pre-1900 English-language texts with full metadata.
+The training corpus for [GPT-1900](https://huggingface.co/mhla/gpt1900-d34-22btok) — a cleaned collection of pre-1900 English-language texts with full metadata. Every document in this corpus was published before the year 1900.
 
 ## Schema
 
@@ -27,8 +35,8 @@ Cleaned corpus of pre-1900 English-language texts with full metadata.
 | `year` | int64 | Publication year |
 | `title` | string | Book title or newspaper name |
 | `source` | string | Source dataset identifier |
-| `ocr_score` | float64 | OCR confidence score (-1.0 if not available) |
-| `legibility` | float64 | Legibility score (-1.0 if not available) |
+| `ocr_score` | float64 | OCR confidence score (-1.0 if unavailable) |
+| `legibility` | float64 | Legibility score (-1.0 if unavailable) |
 
 ## Sources
 
@@ -36,16 +44,24 @@ Cleaned corpus of pre-1900 English-language texts with full metadata.
 - **British Library books** — TheBritishLibrary/blbooks
 - **Historical newspapers** — dell-research-harvard/AmericanStories
 
-## Filtering methodology
+## Filtering Pipeline
 
-Documents were cleaned and filtered through a multi-stage pipeline:
-
-1. **OCR cleanup** — removal of common OCR artifacts, Google/HathiTrust boilerplate,
-   library stamps, and unicode normalization
-2. **Quality filtering** — token frequency prior-based filtering as a cheap proxy
-   for perplexity, removing garbled or low-quality OCR output
-3. **Anachronism detection** — three-tier post-1900 physics filter to remove
-   mislabeled modern texts:
+1. **OCR cleanup** — removal of OCR artifacts, boilerplate, and unicode normalization
+2. **Quality filtering** — token frequency prior-based filtering as a cheap proxy for perplexity
+3. **Anachronism detection** — three-tier post-1900 physics filter to remove mislabeled modern texts:
    - *Always reject*: unambiguous post-1900 terms (photon, spacetime, transistor, etc.)
    - *Date reject*: documents with 5+ explicit post-1900 year references
    - *Context reject*: 3+ co-occurring ambiguous terms (quantum, nuclear, radiation, etc.)
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("mhla/pre1900-corpus")
+```
+
+## Related
+
+- [mhla/gpt1900-d34-22btok](https://huggingface.co/mhla/gpt1900-d34-22btok) — GPT-1900 base model trained on this corpus
+- [mhla/gpt1900-physics-clm](https://huggingface.co/datasets/mhla/gpt1900-physics-clm) — Physics texts for continued pretraining
+- [mhla/gpt1900-instruct-v3-data](https://huggingface.co/datasets/mhla/gpt1900-instruct-v3-data) — Instruction-tuning data