Create README.md

README.md

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+Dataset derived from FUNSD for training GLiNER-based multi-modal models.
+
+Processing script:
+```python
+from datasets import load_dataset
+import shutil
+import json
+import os
+
+dataset = load_dataset("nielsr/funsd")
+raw_labels = dataset['train'].features['ner_tags'].feature.names
+
+gliner_pdf_dataset = []
+output_dir = "gliner_funsd"
+os.makedirs(output_dir, exist_ok=True)
+os.makedirs(os.path.join(output_dir, 'images'), exist_ok=True)
+
+def process_dataset(example):
+    tokens = example['words']
+    bboxes  = example['bboxes']
+    img_src = example['image_path']
+
+    ner_spans = []
+    prev_label = 'O'
+    prev_idx   = None
+
+    for idx, tag_id in enumerate(example['ner_tags']):
+        lbl = raw_labels[tag_id]
+        if lbl == 'O':
+            if prev_label != 'O':
+                ner_spans.append([prev_idx, idx - 1, prev_label])
+                prev_label = 'O'
+            continue
+
+        _, label = lbl.split('-', 1)
+        if prev_label != label:
+            if prev_label != 'O':
+                ner_spans.append([prev_idx, idx - 1, prev_label])
+            prev_idx   = idx
+            prev_label = label
+
+    if prev_label != 'O':
+        ner_spans.append([prev_idx, len(example['ner_tags']) - 1, prev_label])
+
+    fname      = os.path.basename(img_src)
+    img_dst    = os.path.join(output_dir, 'images', fname)
+    shutil.copy(img_src, img_dst)
+
+    gliner_pdf_dataset.append({
+        "tokenized_text":     tokens,
+        "bboxes":     bboxes,
+        "ner":  ner_spans,
+        "image_path": img_dst
+    })
+
+for ex in dataset['train']:
+    process_dataset(ex)
+
+with open(os.path.join(output_dir, 'data.json'), 'w') as f:
+    json.dump(gliner_pdf_dataset, f)
+```