Update README.md

README.md

@@ -93,3 +93,99 @@ generated_texts = processor.batch_decode(
 
 print(generated_texts[0])
 """
+
+#### Transformers:
+
+
+```import json
+import time
+from pathlib import Path
+
+import yaml
+
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import SmolDoclingOptions, VlmPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling.pipeline.vlm_pipeline import VlmPipeline
+
+sources = [
+    # "https://arxiv.org/pdf/2408.09869",
+    "tests/data/2305.03393v1-pg9-img.png",
+    # "tests/data/2305.03393v1-pg9.pdf",
+]
+
+pipeline_options = VlmPipelineOptions()  # artifacts_path="~/local_model_artifacts/"
+pipeline_options.generate_page_images = True
+# If force_backend_text = True, text from backend will be used instead of generated text
+pipeline_options.force_backend_text = False
+
+
+vlm_options = SmolDoclingOptions(
+    # question="Convert this page to docling.",
+    # load_in_8bit=True,
+    # llm_int8_threshold=6.0,
+    # quantized=False,
+)
+
+pipeline_options.vlm_options = vlm_options
+
+from docling_core.types.doc import DocItemLabel, ImageRefMode
+from docling_core.types.doc.document import DEFAULT_EXPORT_LABELS
+
+converter = DocumentConverter(
+    format_options={
+        InputFormat.PDF: PdfFormatOption(
+            pipeline_cls=VlmPipeline,
+            pipeline_options=pipeline_options,
+        ),
+        InputFormat.IMAGE: PdfFormatOption(
+            pipeline_cls=VlmPipeline,
+            pipeline_options=pipeline_options,
+        ),
+    }
+)
+
+out_path = Path("scratch")
+out_path.mkdir(parents=True, exist_ok=True)
+
+for source in sources:
+    start_time = time.time()
+    print("================================================")
+    print("Processing... {}".format(source))
+    print("================================================")
+    print("")
+
+    res = converter.convert(source)
+
+    print("------------------------------------------------")
+    print("MD:")
+    print("------------------------------------------------")
+    print("")
+    print(res.document.export_to_markdown())
+
+    # with (out_path / f"{res.input.file.stem}.html").open("w") as fp:
+    #     fp.write(res.document.export_to_html())
+
+    res.document.save_as_html(
+        filename=Path("{}/{}.html".format(out_path, res.input.file.stem)),
+        image_mode=ImageRefMode.REFERENCED,
+        labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE],
+    )
+
+    with (out_path / f"{res.input.file.stem}.json").open("w") as fp:
+        fp.write(json.dumps(res.document.export_to_dict()))
+
+    with (out_path / f"{res.input.file.stem}.yaml").open("w") as fp:
+        fp.write(yaml.safe_dump(res.document.export_to_dict()))
+
+    pg_num = res.document.num_pages()
+
+    print("")
+    inference_time = time.time() - start_time
+    print(
+        f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}"
+    )
+
+print("================================================")
+print("done!")
+print("================================================")```