Clean up benchmarks

README.md

@@ -219,11 +219,11 @@ rendered = converter("FILEPATH")
 text, _, images = text_from_rendered(rendered)
 ```
 
-This takes all the same configuration as the PdfConverter.  You can specify the configuration `--force_layout_block=Table` to avoid layout detection and instead assume every page is a table.
+This takes all the same configuration as the PdfConverter.  You can specify the configuration `force_layout_block=Table` to avoid layout detection and instead assume every page is a table.  Set `output_format=json` to also get cell bounding boxes.
 
 You can also run this via the CLI with 
 ```shell
-python convert_single.py FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter
+marker_single FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter --output_format json
 ```
 
 # Output Formats

benchmarks/overall/inference.py

@@ -1,47 +1,47 @@
-import io
-
-import fitz as pymupdf
+import json
 import tempfile
 from bs4 import BeautifulSoup
 
+from benchmarks.overall.scoring import score_blocks
+from benchmarks.overall.schema import BlockScores
 from marker.converters.pdf import PdfConverter
 
-def open_pymupdf(pdf_bytes):
-    stream = io.BytesIO(pdf_bytes)
-    return pymupdf.open(stream=stream)
+def get_marker_html(marker_models: dict, pdf_bytes: bytes):
+    block_converter = PdfConverter(
+        artifact_dict=marker_models,
+        config={"page_range": [0], "disable_tqdm": True},
+        renderer="marker.renderers.html.HTMLRenderer"
+    )
+    with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb") as f:
+        f.write(pdf_bytes)
+        rendered = block_converter(f.name)
+    html = rendered.html
+    soup = BeautifulSoup(html, "html.parser")
+    inner_html = str(soup.find("body").decode_contents())
+    return inner_html
+
+
+def marker_html_func(model_dict, sample, **kwargs) -> BlockScores:
+    gt_blocks = json.loads(sample["gt_blocks"])
+    pdf_bytes = sample["pdf"]  # This is a single page PDF
+    marker_html = get_marker_html(model_dict, pdf_bytes)
+    gt_html = [block["html"] for block in gt_blocks]
+    scores = score_blocks(gt_html, marker_html)
+    return scores
 
-def clip_pdf_to_bbox(doc, bbox, padding=1):
-    page = doc[0]
-    height, width = page.bound().height, page.bound().width
-    remove_left = [0, 0, bbox[0] - padding, height]
-    remove_top = [0, 0, width, bbox[1] - padding]
-    remove_right = [bbox[2] + padding, 0, width, height]
-    remove_bottom = [0, bbox[3] + padding, width, height]
-    for remove in [remove_left, remove_top, remove_right, remove_bottom]:
-        clip_rect = pymupdf.Rect(*remove)
-        page.add_redact_annot(clip_rect)
-    page.apply_redactions()
 
-    clip_rect = pymupdf.Rect(*bbox)
-    page.set_cropbox(clip_rect)
-    return doc
+def mathpix_html_func(model_dict, sample, mathpix_ds, **kwargs) -> BlockScores:
+    uuid = sample["uuid"]
+    data = None
+    for row in mathpix_ds:
+        if str(row["uuid"]) == str(uuid):
+            data = row
+            break
+    if not data:
+        raise ValueError(f"Could not find data for uuid {uuid}")
 
-def get_marker_block_html(marker_models: dict, gt_blocks: list, pdf_bytes: bytes):
-    block_html = []
-    for block in gt_blocks:
-        bbox = block["bbox"]
-        doc2 = open_pymupdf(pdf_bytes)
-        clip_pdf_to_bbox(doc2, bbox)
-        block_converter = PdfConverter(
-            artifact_dict=marker_models,
-            config={"page_range": [0], "force_layout_block": block["block_type"], "disable_tqdm": True},
-            renderer="marker.renderers.html.HTMLRenderer"
-        )
-        with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb") as f:
-            doc2.save(f)
-            rendered = block_converter(f.name)
-        html = rendered.html
-        soup = BeautifulSoup(html, "html.parser")
-        inner_html = str(soup.find("body").decode_contents())
-        block_html.append(inner_html)
-    return block_html
+    mathpix_md = data["md"]
+    gt_blocks = json.loads(sample["gt_blocks"])
+    gt_html = [block["html"] for block in gt_blocks]
+    scores = score_blocks(gt_html, mathpix_md, convert=False)
+    return scores

benchmarks/overall/overall.py

@@ -1,5 +1,6 @@
 import json
 import os
+import traceback
 from collections import defaultdict
 from pathlib import Path
 
@@ -8,64 +9,53 @@ import datasets
 import tabulate
 from tqdm import tqdm
 
+from benchmarks.overall.inference import marker_html_func, mathpix_html_func
+from benchmarks.overall.schema import FullResult
 from marker.logger import configure_logging
 from marker.models import create_model_dict
-from inference import get_marker_block_html
 from marker.settings import settings
-from scoring import score_blocks
 
 configure_logging()
 
-@click.command(help="Benchmark PDF to MD conversion.")
-@click.option("--dataset", type=str, help="Path to the benchmark dataset", default="datalab-to/marker_benchmark")
-@click.option("--other_methods", type=str, help="Comma separated list of other methods to compare against.  Possible values:", default="")
-@click.option("--result_path", type=str, default=os.path.join(settings.OUTPUT_DIR, "benchmark", "overall"), help="Output path for results.")
-@click.option("--max_rows", type=int, default=None, help="Maximum number of rows to process.")
-def main(
-        dataset: str,
-        other_methods: str,
-        result_path: str,
-        max_rows: int
-):
-    allowed_methods = [""]
-    methods = other_methods.split(",")
-    for method in methods:
-        if method not in allowed_methods:
-            raise ValueError(f"Method {method} not allowed.  Allowed methods are {allowed_methods}")
-
-    model_dict = create_model_dict()
-    ds = datasets.load_dataset(dataset, split="train")
 
+def get_method_scores(ds, model_dict, max_rows=None, html_func=marker_html_func, **kwargs) -> FullResult:
     bench_scores = {}
     averages_by_type = defaultdict(list)
     averages_by_block_type = defaultdict(list)
     for idx, sample in tqdm(enumerate(ds), desc="Running benchmark"):
+        if max_rows is not None and idx >= max_rows:
+            break
+
         gt_blocks = json.loads(sample["gt_blocks"])
         doc_type = sample["classification"]
-        pdf_bytes = sample["pdf"] # This is a single page PDF
-        marker_html = get_marker_block_html(model_dict, gt_blocks, pdf_bytes)
-        gt_html = [block["html"] for block in gt_blocks]
-        scores = score_blocks(gt_html, marker_html)
-        gt_weights = [len(ht) for ht in gt_html]
-        overall_score = sum([s * w for s, w in zip(scores, gt_weights)]) / sum(gt_weights)
-        bench_scores[idx] = {
-            "scores": scores,
-            "weights": gt_weights,
-            "overall_score": overall_score # Weighted score, weighted by length of GT block
-        }
-
-        averages_by_type[doc_type].append(overall_score)
-
-        for score, gt_block in zip(scores, gt_blocks):
+        try:
+            scores = html_func(model_dict, sample, **kwargs)
+        except ValueError as e:
+            print(f"Error with sample {idx}: {e}")
+            continue
+        averages_by_type[doc_type].append(scores["overall_score"])
+
+        for score, gt_block in zip(scores["scores"], gt_blocks):
             averages_by_block_type[gt_block["block_type"]].append(score)
 
-        if max_rows is not None and idx >= max_rows:
-            break
+        bench_scores[idx] = scores
+
+    return {
+        "raw_scores": bench_scores,
+        "averages_by_type": averages_by_type,
+        "averages_by_block_type": averages_by_block_type
+    }
+
+def print_scores(scores: FullResult, method: str):
+    averages_by_type = scores["averages_by_type"]
+    averages_by_block_type = scores["averages_by_block_type"]
+    bench_scores = scores["raw_scores"]
 
     for k in averages_by_type:
         averages_by_type[k] = sum(averages_by_type[k]) / len(averages_by_type[k])
     averages_by_type = sorted(averages_by_type.items())
 
+    print(f"Scores for method {method}:")
     print(tabulate.tabulate(averages_by_type, headers=["Document Type", "Average Score"], tablefmt="github"))
 
     for k in averages_by_block_type:
@@ -76,10 +66,45 @@ def main(
 
     overall_average = sum([bench_scores[k]["overall_score"] for k in bench_scores]) / len(bench_scores)
     print(tabulate.tabulate([["Overall Average", overall_average]], tablefmt="github"))
+    print()
+
+@click.command(help="Benchmark PDF to MD conversion.")
+@click.option("--dataset", type=str, help="Path to the benchmark dataset", default="datalab-to/marker_benchmark")
+@click.option("--other_methods", type=str, help="Comma separated list of other methods to compare against.  Possible values: mathpix", default="")
+@click.option("--result_path", type=str, default=os.path.join(settings.OUTPUT_DIR, "benchmark", "overall"), help="Output path for results.")
+@click.option("--max_rows", type=int, default=None, help="Maximum number of rows to process.")
+def main(
+        dataset: str,
+        other_methods: str,
+        result_path: str,
+        max_rows: int
+):
+    allowed_methods = ["mathpix", ""]
+    methods = other_methods.split(",")
+    for method in methods:
+        if method not in allowed_methods:
+            raise ValueError(f"Method {method} not allowed.  Allowed methods are {allowed_methods}")
+
+    model_dict = create_model_dict()
+    ds = datasets.load_dataset(dataset, split="train")
+
+    marker_scores = get_method_scores(ds, model_dict, max_rows=max_rows)
+    all_scores = {
+        "marker": marker_scores
+    }
+
+    if "mathpix" in methods:
+        mathpix_ds = datasets.load_dataset("datalab-to/marker_benchmark_mathpix", split="train")
+        mathpix_scores = get_method_scores(ds, model_dict, max_rows=max_rows, html_func=mathpix_html_func, mathpix_ds=mathpix_ds)
+        all_scores["mathpix"] = mathpix_scores
+
+    for k,v in all_scores.items():
+        print_scores(v, k)
 
-    out_path = Path(result_path) / "overall.json"
-    with open(out_path, "w") as f:
-        json.dump(bench_scores, f, indent=2)
+    out_path = Path(result_path)
+    out_path.mkdir(parents=True, exist_ok=True)
+    with open(out_path / "overall.json", "w") as f:
+        json.dump(all_scores, f, indent=2)
 
     print(f"Results saved to {out_path}.")
 

benchmarks/overall/schema.py

@@ -0,0 +1,15 @@
+from typing import TypedDict, List, Dict
+
+
+class BlockScores(TypedDict):
+    scores: List[float]
+    order_score: float
+    gt: List[str]
+    method: str
+    overall_score: float
+
+
+class FullResult(TypedDict):
+    raw_scores: Dict[int, BlockScores]
+    averages_by_type: Dict[str, List[float]]
+    averages_by_block_type: Dict[str, List[float]]

benchmarks/overall/scoring.py

@@ -1,30 +1,135 @@
-import re
-from bs4 import BeautifulSoup
+from typing import List
 
-from markdownify import markdownify as md
 from rapidfuzz import fuzz
 
-def standardize_html(html):
-    soup = BeautifulSoup(html, "html.parser")
+from benchmarks.overall.schema import BlockScores
+from marker.renderers.markdown import MarkdownRenderer
+import re
+
+
+def kendall_tau(correct_order: List[int], actual_order: List[int]) -> float:
+    n = len(correct_order)
+    concordant = 0
+    discordant = 0
+
+    for i in range(n):
+        for j in range(i + 1, n):
+            correct_sign = correct_order[i] - correct_order[j]
+            actual_sign = actual_order[i] - actual_order[j]
+
+            if (correct_sign > 0 and actual_sign > 0) or (correct_sign < 0 and actual_sign < 0):
+                concordant += 1
+            elif (correct_sign < 0 and actual_sign > 0) or (correct_sign > 0 and actual_sign < 0):
+                discordant += 1
+
+    total_pairs = (n * (n - 1)) // 2
+    tau = (concordant - discordant) / total_pairs
+    tau = (tau + 1) / 2 # 0-1 scale
+    return tau * 100 # 0-100 scale
+
+
+def find_fuzzy_alignments(
+        main_string: str,
+        substrings: List[str],
+        threshold: int = 70
+) -> List[dict]:
+    alignments = []
+
+    for idx, substr in enumerate(substrings):
+        result = fuzz.partial_ratio_alignment(substr, main_string, score_cutoff=threshold)
+
+        score = 0
+        dest_start = 0
+        dest_end = 0
+        if result:
+            score = result.score
+            dest_start = result.dest_start
+            dest_end = result.dest_end
+
+        alignments.append({
+            "string": substr,
+            "start": dest_start,
+            "end": dest_end,
+            "score": score,
+            "idx": idx
+        })
+    return alignments
 
-    # Convert all headers to h1 so we don't penalize small differences in header levels
-    for tag in soup.find_all(["h1", "h2", "h3", "h4", "h5", "h6"]):
-        tag.name = "h1"
+def convert_to_md(html):
+    md = MarkdownRenderer()
+    markdown = md.md_cls.convert(html)
+    return markdown
+
+def standardize_markdown(markdown):
+    pattern = r'(?<!\\)\$(?:\$([^$]+)\$\$|\s*([^$\n]+?)\s*\$)'
+    markdown = re.sub(pattern, standardize_math, markdown)
 
-    html = str(soup)
-    markdown = md(html)
     markdown = markdown.replace("<br>", "\n")
+    markdown = re.sub(r"<sub>(.*?)</sub>", r"\1", markdown)
+    markdown = re.sub(r"<sup>(.*?)</sup>", r"\1", markdown)
+
     markdown = re.sub(r"\s+", " ", markdown)
     markdown = re.sub(r"\n+", "\n", markdown)
     markdown = re.sub("\\.+", ".", markdown) # Replace repeated periods with a single period, like in table of contents
-    return markdown.strip()
+    markdown = re.sub("#+", "#", markdown) # Replace repeated headers with a single header
+    markdown = re.sub(r"\$", "", markdown) # Remove equation delimiters
+    return markdown.strip().lower()
+
+
+def standardize_math(match):
+    try:
+        delim = "$$" if match.group(0).startswith('$$') else "$"
+        math_content = match.group(1) or match.group(2)
+        result = clean_latex(math_content)
+        return f'{delim}{result}{delim}'
+    except Exception as e:
+        print(f"Failed to standardize math expression: {match.group(0)} with error: {e}")
+        return match.group(0)
+
+
+def clean_latex(latex_str):
+    latex_str = re.sub(r'\s+', ' ', latex_str.strip())
+    for tag in [r'\\text', r'\\mathrm', r'\\mathbf', r'\\textbf']:
+        latex_str = re.sub(tag + r'\{([^}]+)\}', r'\1', latex_str)
+
+
+    replacements = {
+        '\\times': '*',
+        '\\cdot': '*',
+        '\\div': '/',
+        '\\le': '<=',
+        '\\ge': '>=',
+        '\\neq': '!=',
+        '\\to': '\\rightarrow',
+    }
+
+    for old, new in replacements.items():
+        latex_str = latex_str.replace(old, new)
+
+    return latex_str
+
 
+def score_blocks(gt_html, method_html, convert=True) -> BlockScores:
+    if convert:
+        method_html = convert_to_md(method_html)
+    method_html = standardize_markdown(method_html)
+    gt = [standardize_markdown(convert_to_md(gt)) for gt in gt_html]
+    alignments = find_fuzzy_alignments(method_html, gt)
+    scores = [alignment["score"] for alignment in alignments]
+    orders = [alignment["start"] for alignment in alignments]
+    correct_order = range(len(gt))
+    actual_order = sorted(range(len(gt)), key=lambda x: orders[x])
+    order_score = kendall_tau(correct_order, actual_order)
+    gt_weights = [len(g) for g in gt]
+    weighted_scores = [score * weight for score, weight in zip(scores, gt_weights)]
 
-def score_blocks(gt_html, method_html):
-    scores = []
-    for gt, method in zip(gt_html, method_html):
-        gt= standardize_html(gt)
-        method = standardize_html(method)
-        score = fuzz.ratio(gt, method)
-        scores.append(score)
-    return scores
+    # Weight the score by sequence length
+    overall_score = sum(weighted_scores) / max(1, sum(gt_weights))
+    overall_score = overall_score * 0.8 + order_score * 0.2
+    return {
+        "scores": scores,
+        "order_score": order_score,
+        "gt": gt,
+        "method": method_html,
+        "overall_score": overall_score
+    }

benchmarks/table/table.py

@@ -223,6 +223,7 @@ def main(
     }
 
     out_path = Path(result_path) / "table.json"
+    out_path.mkdir(parents=True, exist_ok=True)
     with open(out_path, "w+") as f:
         json.dump(results, f, indent=2)
 

marker/processors/llm/llm_complex.py

@@ -85,4 +85,4 @@ Output:
 
         # Convert LLM markdown to html
         corrected_markdown = corrected_markdown.strip().lstrip("```markdown").rstrip("```").strip()
-        block.html = markdown2.markdown(corrected_markdown)
+        block.html = markdown2.markdown(corrected_markdown, extras=["tables"])

marker/processors/llm/llm_handwriting.py

@@ -72,4 +72,4 @@ Formatting should be in markdown, with the following rules:
             return
 
         markdown = markdown.strip().lstrip("```markdown").rstrip("```").strip()
-        block.html = markdown2.markdown(markdown)
+        block.html = markdown2.markdown(markdown, extras=["tables"])

marker/renderers/markdown.py

@@ -198,10 +198,9 @@ class MarkdownRenderer(HTMLRenderer):
     inline_math_delimiters: Annotated[Tuple[str], "The delimiters to use for inline math."] = ("$", "$")
     block_math_delimiters: Annotated[Tuple[str], "The delimiters to use for block math."] = ("$$", "$$")
 
-    def __call__(self, document: Document) -> MarkdownOutput:
-        document_output = document.render()
-        full_html, images = self.extract_html(document, document_output)
-        md_cls = Markdownify(
+    @property
+    def md_cls(self):
+        return Markdownify(
             self.paginate_output,
             self.page_separator,
             heading_style="ATX",
@@ -215,7 +214,12 @@ class MarkdownRenderer(HTMLRenderer):
             inline_math_delimiters=self.inline_math_delimiters,
             block_math_delimiters=self.block_math_delimiters
         )
-        markdown = md_cls.convert(full_html)
+
+
+    def __call__(self, document: Document) -> MarkdownOutput:
+        document_output = document.render()
+        full_html, images = self.extract_html(document, document_output)
+        markdown = self.md_cls.convert(full_html)
         markdown = cleanup_text(markdown)
         return MarkdownOutput(
             markdown=markdown,

poetry.lock

@@ -801,13 +801,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastapi"
-version = "0.115.7"
+version = "0.115.8"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.115.7-py3-none-any.whl", hash = "sha256:eb6a8c8bf7f26009e8147111ff15b5177a0e19bb4a45bc3486ab14804539d21e"},
-    {file = "fastapi-0.115.7.tar.gz", hash = "sha256:0f106da6c01d88a6786b3248fb4d7a940d071f6f488488898ad5d354b25ed015"},
+    {file = "fastapi-0.115.8-py3-none-any.whl", hash = "sha256:753a96dd7e036b34eeef8babdfcfe3f28ff79648f86551eb36bfc1b0bf4a8cbf"},
+    {file = "fastapi-0.115.8.tar.gz", hash = "sha256:0ce9111231720190473e222cdf0f07f7206ad7e53ea02beb1d2dc36e2f0741e9"},
 ]
 
 [package.dependencies]
@@ -1367,13 +1367,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.28.0"
+version = "0.28.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.28.0-py3-none-any.whl", hash = "sha256:71cff4e500efe68061d94b7f6d3114e183715088be7a90bf4dd84af83b5f5cdb"},
-    {file = "huggingface_hub-0.28.0.tar.gz", hash = "sha256:c2b18c02a47d4384763caddb4d0ab2a8fc6c16e0800d6de4d55d0a896244aba3"},
+    {file = "huggingface_hub-0.28.1-py3-none-any.whl", hash = "sha256:aa6b9a3ffdae939b72c464dbb0d7f99f56e649b55c3d52406f49e0a5a620c0a7"},
+    {file = "huggingface_hub-0.28.1.tar.gz", hash = "sha256:893471090c98e3b6efbdfdacafe4052b20b84d59866fb6f54c33d9af18c303ae"},
 ]
 
 [package.dependencies]
@@ -1826,13 +1826,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.3.4"
+version = "4.3.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.3.4-py3-none-any.whl", hash = "sha256:b754c2601c5be6adf87cb5a1d8495d653ffb945f021939f77776acaa94dae952"},
-    {file = "jupyterlab-4.3.4.tar.gz", hash = "sha256:f0bb9b09a04766e3423cccc2fc23169aa2ffedcdf8713e9e0fb33cac0b6859d0"},
+    {file = "jupyterlab-4.3.5-py3-none-any.whl", hash = "sha256:571bbdee20e4c5321ab5195bc41cf92a75a5cff886be5e57ce78dfa37a5e9fdb"},
+    {file = "jupyterlab-4.3.5.tar.gz", hash = "sha256:c779bf72ced007d7d29d5bcef128e7fdda96ea69299e19b04a43635a7d641f9d"},
 ]
 
 [package.dependencies]
@@ -2729,18 +2729,6 @@ files = [
 [package.dependencies]
 nvidia-nvjitlink-cu12 = "*"
 
-[[package]]
-name = "nvidia-cusparselt-cu12"
-version = "0.6.2"
-description = "NVIDIA cuSPARSELt"
-optional = false
-python-versions = "*"
-files = [
-    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:067a7f6d03ea0d4841c85f0c6f1991c5dda98211f6302cb83a4ab234ee95bef8"},
-    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:df2c24502fd76ebafe7457dbc4716b2fec071aabaed4fb7691a201cde03704d9"},
-    {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-win_amd64.whl", hash = "sha256:0057c91d230703924c0422feabe4ce768841f9b4b44d28586b6f6d2eb86fbe70"},
-]
-
 [[package]]
 name = "nvidia-nccl-cu12"
 version = "2.21.5"
@@ -3578,23 +3566,6 @@ files = [
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 [package.dependencies]
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 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.13.0)"]
+optree = ["optree (>=0.12.0)"]
 
 [[package]]
 name = "tornado"
@@ -5054,18 +5021,21 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
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+    {file = "triton-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6b0dd10a925263abbe9fa37dcde67a5e9b2383fc269fdf59f5657cac38c5d1d8"},
+    {file = "triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c"},
+    {file = "triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc"},
+    {file = "triton-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dadaca7fc24de34e180271b5cf864c16755702e9f63a16f62df714a8099126a"},
+    {file = "triton-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aafa9a20cd0d9fee523cd4504aa7131807a864cd77dcf6efe7e981f18b8c6c11"},
 ]
 
+[package.dependencies]
+filelock = "*"
+
 [package.extras]
 build = ["cmake (>=3.20)", "lit"]
 tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
@@ -5498,4 +5468,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "9d330f12a8bad0352ec550e1d6a77348b10f6bca7ecc41769813bec85d3f9e08"
+content-hash = "9060b047f34d36d3ee1850cbbbaf2078fe14471661117b786c4e9a7661dc659a"

pyproject.toml

@@ -22,7 +22,7 @@ pydantic = "^2.4.2"
 pydantic-settings = "^2.0.3"
 transformers = "^4.45.2"
 python-dotenv = "^1.0.0"
-torch = "^2.5.1"
+torch = "~2.5.1" # 2.6.0 appears to fail with mps
 tqdm = "^4.66.1"
 ftfy = "^6.1.1"
 rapidfuzz = "^3.8.1"
@@ -49,7 +49,6 @@ apted = "1.0.3"
 distance = "0.1.3"
 lxml = "5.3.0"
 tabulate = "^0.9.0"
-pymupdf = "^1.25.2"
 
 [tool.poetry.scripts]
 marker = "marker.scripts.convert:convert_cli"