docs: Parse Finetuning Tutorial

examples/vlm_finetune/nemotron/parse-ft-tutorial/invoice_dataset.py

@@ -0,0 +1,56 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+
+from datasets import load_dataset
+
+
+def json2token(obj, sort_json_key=True):
+    if isinstance(obj, dict):
+        if len(obj) == 1 and "text_sequence" in obj:
+            return obj["text_sequence"]
+        out = ""
+        keys = sorted(obj.keys()) if sort_json_key else obj.keys()
+        for k in keys:
+            out += f"<s_{k}>" + json2token(obj[k], sort_json_key) + f"</s_{k}>"
+        return out
+    if isinstance(obj, list):
+        return "<sep/>".join(json2token(i, sort_json_key) for i in obj)
+    return str(obj)
+
+
+def make_invoice_dataset(path_or_dataset="katanaml-org/invoices-donut-data-v1", split="train", **kwargs):
+    ds = load_dataset(path_or_dataset, split=split)
+    samples = []
+    for ex in ds:
+        gt = json.loads(ex["ground_truth"])["gt_parse"]
+        target = json2token(gt)
+        samples.append(
+            {
+                "conversation": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image", "image": ex["image"]},
+                            {"type": "text", "text": "Parse this invoice."},
+                        ],
+                    },
+                    {"role": "assistant", "content": [{"type": "text", "text": target}]},
+                ],
+            }
+        )
+    print(f"{split.capitalize()} samples: {len(samples)}")
+    if samples:
+        print(f"Target preview: {samples[0]['conversation'][1]['content'][0]['text'][:200]}...")
+    return samples