Upload README.md

README.md

@@ -1,17 +1,73 @@
 ---
 license: other
 language:
-- en
+  - en
 tags:
-- wto
-- trade
-- legal
-- dispute-settlement
-- international-law
-- rag
+  - wto
+  - trade
+  - legal
+  - dispute-settlement
+  - international-law
+  - rag
 pretty_name: WTO Dispute Settlement Body Documents
 size_categories:
-- 10K<n<100K
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: wto_documents_full.jsonl
+dataset_info:
+  features:
+    - name: folder_number
+      dtype: string
+    - name: case_number
+      dtype: string
+    - name: original_filename
+      dtype: string
+    - name: new_filename
+      dtype: string
+    - name: doc_sequence
+      dtype: int32
+    - name: doc_type
+      dtype: string
+    - name: doc_type_raw
+      dtype: string
+    - name: doc_class
+      dtype: string
+    - name: variant
+      dtype: string
+    - name: part_number
+      dtype: int32
+    - name: case_title
+      dtype: string
+    - name: date
+      dtype: string
+    - name: header_codes
+      dtype: string
+    - name: agreement_indicators
+      dtype: string
+    - name: complainant
+      dtype: string
+    - name: respondent
+      dtype: string
+    - name: third_parties
+      dtype: string
+    - name: dispute_stage
+      dtype: string
+    - name: agreements_cited
+      dtype: string
+    - name: case_summary
+      dtype: string
+    - name: page_count
+      dtype: int32
+    - name: clean_text
+      dtype: string
+    - name: processing_date
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 9414
 ---
 
 # WTO Dispute Settlement Body Documents
@@ -125,6 +181,36 @@ OCR (Tesseract) was applied as a fallback for 78 scanned PDFs where PyPDF extrac
 
 ## Usage Example
 
+### With the HuggingFace `datasets` library (recommended)
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+ds = load_dataset("dean22029/WTO_Docs")
+df = ds["train"].to_pandas()
+
+# Filter to a single case
+ds267 = ds["train"].filter(lambda x: x["case_number"] == "267")
+
+# Filter by document type
+consultations = ds["train"].filter(
+    lambda x: x["doc_type"] == "Request_For_Consultations"
+)
+
+# Filter by dispute stage
+appellate = ds["train"].filter(
+    lambda x: x["dispute_stage"] == "Appellate Body"
+)
+
+# Access a record
+print(ds["train"][0]["case_number"])   # "1"
+print(ds["train"][0]["doc_type"])      # "Request_For_Consultations"
+print(ds["train"][0]["clean_text"][:300])
+```
+
+### With plain Python (no dependencies)
+
 ```python
 import json
 
@@ -136,7 +222,7 @@ with open("wto_documents_full.jsonl") as f:
         break
 ```
 
-**Load all consultation requests for a specific case:**
+**Load all documents for a specific case:**
 
 ```python
 import json
@@ -151,9 +237,22 @@ def get_case_docs(jsonl_path, case_number, doc_type=None):
                     docs.append(doc)
     return docs
 
+# All documents for DS267 (EC - Beef Hormones)
+all_docs = get_case_docs("wto_documents_full.jsonl", 267)
+
+# Consultation requests only
 consultations = get_case_docs("wto_documents_full.jsonl", 267, "Request_For_Consultations")
 ```
 
+**Parse the `third_parties` field:**
+
+```python
+import ast
+
+doc = json.loads(line)
+third_parties = ast.literal_eval(doc["third_parties"])  # e.g. ["USA", "Canada"]
+```
+
 ## Data Source and Processing
 
 Documents were scraped from the [WTO Dispute Settlement Gateway](https://www.wto.org/english/tratop_e/dispu_e/dispu_e.htm) using Selenium. PDFs were parsed with PyPDFLoader (text-based) and Tesseract OCR (scanned). Dates were extracted multilingually (English, French, Spanish) from PDF headings.
@@ -166,4 +265,4 @@ Documents were scraped from the [WTO Dispute Settlement Gateway](https://www.wto
 - `date` is `null` for ~4.5% of records (mostly untitled addenda and cross-reference files).
 - Non-English documents (primarily French/Spanish originals) have reduced `clean_text` quality after line-level language filtering.
 - Taiwan (`Chinese Taipei`) has no UN ideal point data in linked panel datasets — expected, as it is not a UN member.
-- DS627+ cases exist in case metadata but have no associated PDFs in this corpus (collection cutoff: DS626).
+- DS627+ cases exist in case metadata but have no associated PDFs in this corpus (collection cutoff: DS626).