repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
mmda | mmda-main/src/mmda/predictors/d2_predictors/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/predictors/d2_predictors/bibentry_detection_predictor.py | from functools import reduce
import itertools
from typing import Any, Dict, Iterator, List, Optional, Union
import layoutparser as lp
from mmda.predictors.base_predictors.base_predictor import BasePredictor
from mmda.types.annotation import BoxGroup
from mmda.types.box import Box
from mmda.types.document import Docum... | 6,337 | 35.011364 | 109 | py |
mmda | mmda-main/src/mmda/predictors/base_predictors/base_predictor.py | from dataclasses import dataclass
from abc import abstractmethod
from typing import Union, List, Dict, Any
from mmda.types.annotation import Annotation
from mmda.types.document import Document
class BasePredictor:
###################################################################
##################### Nece... | 2,192 | 39.611111 | 101 | py |
mmda | mmda-main/src/mmda/predictors/base_predictors/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/predictors/base_predictors/base_heuristic_predictor.py | from mmda.predictors.base_predictors.base_predictor import BasePredictor
class BaseHeuristicPredictor(BasePredictor):
REQUIRED_BACKENDS = [] | 146 | 28.4 | 72 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/vila_predictor.py | # This file rewrites the PDFPredictor classes in
# https://github.com/allenai/VILA/blob/dd242d2fcbc5fdcf05013174acadb2dc896a28c3/src/vila/predictors.py#L1
# to reduce the dependency on the VILA package.
from typing import List, Union, Dict, Any, Tuple
from abc import abstractmethod
from dataclasses import dataclass
im... | 11,250 | 35.060897 | 105 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/utils.py | from mmda.types.annotation import SpanGroup
from typing import List, Tuple, Dict
import itertools
from mmda.types.document import Document
from mmda.types.names import BlocksField, RowsField
def normalize_bbox(
bbox,
page_width,
page_height,
target_width,
target_height,
):
"""
Normalize b... | 3,926 | 29.679688 | 81 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/mention_predictor.py | import itertools
import os.path
import string
from typing import Dict, Iterator, List, Optional
from optimum.onnxruntime import ORTModelForTokenClassification
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer, BatchEncoding
from mmda.types.annotation import Annotation, SpanGroup
fro... | 9,459 | 40.130435 | 132 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/token_classification_predictor.py | from typing import List, Union, Dict, Any, Tuple, Optional, Sequence
from abc import abstractmethod
from tqdm import tqdm
from vila.predictors import (
SimplePDFPredictor,
LayoutIndicatorPDFPredictor,
HierarchicalPDFPredictor,
)
from mmda.types.metadata import Metadata
from mmda.types.names import Blocks... | 5,137 | 34.191781 | 157 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/span_group_classification_predictor.py | """
@kylel
"""
from typing import List, Any, Tuple, Optional, Sequence
from collections import defaultdict
import numpy as np
import torch
import transformers
from smashed.interfaces.simple import (
TokenizerMapper,
UnpackingMapper,
FixedBatchSizeMapper,
FromTokenizerListCollatorMapper,
Python2... | 14,554 | 39.543175 | 99 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/predictors/hf_predictors/base_hf_predictor.py | from abc import abstractmethod
from typing import Union, List, Dict, Any
from transformers import AutoTokenizer, AutoConfig, AutoModel
from mmda.types.document import Document
from mmda.predictors.base_predictors.base_predictor import BasePredictor
class BaseHFPredictor(BasePredictor):
REQUIRED_BACKENDS = ["tra... | 1,206 | 32.527778 | 72 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/bibentry_predictor/predictor.py | import os
import re
from typing import Dict, List, Optional, Tuple
from optimum.onnxruntime import ORTModelForTokenClassification
import torch
from transformers import AutoConfig, AutoTokenizer, AutoModelForTokenClassification
from unidecode import unidecode
from mmda.predictors.hf_predictors.base_hf_predictor import... | 9,946 | 43.806306 | 193 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/bibentry_predictor/utils.py | from typing import List
from mmda.types.annotation import SpanGroup
from mmda.types.document import Document
from mmda.types.span import Span
from mmda.predictors.hf_predictors.bibentry_predictor.types import BibEntryPredictionWithSpan, BibEntryStructureSpanGroups
_SPAN_JOINER = " "
def mk_bib_entry_strings(docume... | 3,298 | 36.91954 | 122 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/bibentry_predictor/types.py | from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional
from pydantic import BaseModel
from mmda.types.annotation import SpanGroup
class BibEntryLabel(Enum):
MISC = 0
CITATION_NUMBER = 1
AUTHOR_START = 2
AUTHOR_MIDDLE = 3
AUTHOR_END = 4
ISSUED_DAY ... | 1,474 | 23.583333 | 75 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/bibentry_predictor/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/figure_table_predictors.py | from collections import defaultdict
from typing import List, Dict, Tuple, Union
import numpy as np
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm
from ai2_internal import api
from mmda.predictors.base_predictors.base_heuristic_predictor import BaseHeuristicPredictor
from mmda.types import Spa... | 21,673 | 51.992665 | 121 | py |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/dictionary_word_predictor.py | """
DictionaryWordPredictor -- Reads `tokens` and converts them into whole `words`.
Let's consider 4 consecutive rows:
This is a few-shot learn-
ing paper. We try few-
shot techniques. These
methods are useful.
This predictor tries to:
1. merge tokens ["learn", "-", "ing"] into "learning"
2. ... | 24,983 | 46.953935 | 106 | py |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/section_header_predictor.py | """
SectionHeaderPredictor -- Use PDF outline metadata to predict section headers and
levels. This predictor is entirely heuristic and only applies to PDFs that have ToC
information in the sidebar. See SectionNestingPredictor for a related class that
operates over token predictions to yield an outline of s... | 8,439 | 31.337165 | 88 | py |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/grobid_citation_predictor.py | """
@rauthur
"""
import io
import xml.etree.ElementTree as et
from typing import Optional
import requests
# processCitationList available in Grobid 0.7.1-SNAPSHOT and later
DEFAULT_API = "http://localhost:8070/api/processCitation"
NS = {"tei": "http://www.tei-c.org/ns/1.0"}
def _post_document(citations: str, url... | 912 | 22.410256 | 87 | py |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/sentence_boundary_predictor.py | from typing import List, Tuple
import itertools
import pysbd
import numpy as np
from mmda.types.annotation import SpanGroup
from mmda.types.span import Span
from mmda.types.document import Document
from mmda.types.names import PagesField, TokensField, WordsField
from mmda.predictors.base_predictors.base_heuristic_pre... | 5,096 | 31.673077 | 172 | py |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/whitespace_predictor.py | """
Uses a whitespace tokenizer on Document.symbols to predict which `tokens` were originally
part of the same segment/chunk (e.g. "few-shot" if tokenized as ["few", "-", "shot"]).
@kylel
"""
from typing import Optional, Set, List, Tuple
import tokenizers
from mmda.predictors.base_predictors.base_predictor import ... | 1,840 | 35.82 | 101 | py |
mmda | mmda-main/src/mmda/predictors/heuristic_predictors/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/recipes/core_recipe.py | """
@kylel
"""
import logging
logger = logging.getLogger(__name__)
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.hf_predictors.token_classification_predictor import (
IVILATokenClassificationPredictor,
)
from mmda.predictors.lp_predictors import LayoutParserPredictor
from mm... | 2,718 | 34.776316 | 137 | py |
mmda | mmda-main/src/mmda/recipes/__init__.py | from mmda.recipes.core_recipe import CoreRecipe
__all__ = [
'CoreRecipe'
] | 79 | 15 | 47 | py |
mmda | mmda-main/src/mmda/recipes/recipe.py | """
@kylel
"""
from mmda.types import *
from abc import abstractmethod
class Recipe:
@abstractmethod
def from_path(self, pdfpath: str) -> Document:
raise NotImplementedError
@abstractmethod
def from_doc(self, doc: Document) -> Document:
raise NotImplementedError
| 303 | 13.47619 | 50 | py |
mmda | mmda-main/src/mmda/featurizers/citation_link_featurizers.py | import numpy as np
import pandas as pd
from pydantic import BaseModel
import re
from thefuzz import fuzz
from typing import List, Tuple, Dict
from mmda.types.annotation import SpanGroup
DIGITS = re.compile(r'[0-9]+')
ALPHA = re.compile(r'[A-Za-z]+')
RELEVANT_PUNCTUATION = re.compile(r"\(|\)|\[|,|\]|\.|&|\;")
FUZZ_R... | 6,480 | 36.680233 | 122 | py |
mmda | mmda-main/src/mmda/featurizers/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/types/document.py | """
"""
import itertools
import logging
import warnings
from typing import Dict, Iterable, List, Optional
from mmda.types.annotation import Annotation, BoxGroup, SpanGroup
from mmda.types.image import PILImage
from mmda.types.indexers import Indexer, SpanGroupIndexer
from mmda.types.metadata import Metadata
from m... | 8,030 | 36.180556 | 108 | py |
mmda | mmda-main/src/mmda/types/box.py | """
"""
from typing import List, Dict, Tuple, Union
from dataclasses import dataclass
import warnings
import numpy as np
def is_overlap_1d(start1: float, end1: float, start2: float, end2: float, x: float = 0) -> bool:
"""Return whether two 1D intervals overlaps given x"""
assert start1 <= end1
assert... | 4,742 | 33.369565 | 120 | py |
mmda | mmda-main/src/mmda/types/image.py | """
Monkey patch the PIL.Image methods to add base64 conversion
"""
import base64
from io import BytesIO
from PIL import Image as pilimage
from PIL.Image import Image as PILImage
def tobase64(self):
# Ref: https://stackoverflow.com/a/31826470
buffered = BytesIO()
self.save(buffered, format="PNG")
... | 832 | 24.242424 | 90 | py |
mmda | mmda-main/src/mmda/types/user_data.py | """
UserData and HasUserData allow a Python class to have an underscore `_` property for
accessing custom fields. Data captured within this property can be serialized along with
any instance of the class.
"""
from typing import Any, Callable, Dict, Optional
class UserData:
_data: Dict[str, Any]
_callback: O... | 1,376 | 26.54 | 88 | py |
mmda | mmda-main/src/mmda/types/metadata.py | from copy import deepcopy
from dataclasses import MISSING, Field, fields, is_dataclass
from functools import wraps
import inspect
import logging
from typing import (
Any,
Callable,
Dict,
Iterable,
Optional,
Tuple,
Type,
TypeVar,
Union,
overload,
)
__all__ = ["store_field_in_met... | 15,513 | 37.306173 | 79 | py |
mmda | mmda-main/src/mmda/types/names.py | """
Names of fields, as strings
@kylel
"""
# document field names
SymbolsField = "symbols"
MetadataField = "metadata"
ImagesField = "images"
PagesField = "pages"
TokensField = "tokens"
RowsField = "rows"
SentencesField = "sents"
BlocksField = "blocks"
WordsField = "words"
SectionsField = "sections"
| 305 | 13.571429 | 27 | py |
mmda | mmda-main/src/mmda/types/span.py | """
"""
from dataclasses import dataclass
from typing import Dict, List, Optional
from mmda.types.box import Box
@dataclass
class Span:
start: int
end: int
box: Optional[Box] = None
def to_json(self) -> Dict:
if self.box:
return dict(start=self.start, end=self.end, box=self.b... | 2,096 | 27.337838 | 84 | py |
mmda | mmda-main/src/mmda/types/__init__.py | from mmda.types.document import Document
from mmda.types.annotation import SpanGroup, BoxGroup
from mmda.types.span import Span
from mmda.types.box import Box
from mmda.types.image import PILImage
from mmda.types.metadata import Metadata
__all__ = [
'Document',
'SpanGroup',
'BoxGroup',
'Span',
'Box... | 355 | 21.25 | 53 | py |
mmda | mmda-main/src/mmda/types/indexers.py | """
Indexes for Annotations
"""
from typing import List
from abc import abstractmethod
from dataclasses import dataclass, field
from mmda.types.annotation import SpanGroup, Annotation
from ncls import NCLS
import numpy as np
import pandas as pd
@dataclass
class Indexer:
"""Stores an index for a particular co... | 3,107 | 30.714286 | 100 | py |
mmda | mmda-main/src/mmda/types/annotation.py | """
Annotations are objects that are 'aware' of the Document
Collections of Annotations are how one constructs a new
Iterable of Group-type objects within the Document
"""
import warnings
from abc import abstractmethod
from copy import deepcopy
from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Union
... | 8,389 | 28.031142 | 84 | py |
mmda | mmda-main/src/mmda/types/old/image.old.py | """
Dataclass for doing stuff on images of pages of a document
@kylel, @shannons
"""
import base64
from io import BytesIO
from PIL import Image
# Monkey patch the PIL.Image methods to add base64 conversion
def tobase64(self):
# Ref: https://stackoverflow.com/a/31826470
buffered = BytesIO()
self.save(... | 798 | 23.96875 | 89 | py |
mmda | mmda-main/src/mmda/types/old/boundingbox.old.py | """
Dataclass for doing stuff on bounding boxes
@kyle
"""
from typing import List, Dict
import json
class BoundingBox:
def __init__(self, l: float, t: float, w: float, h: float, page: int):
"""Assumes x=0.0 and y=0.0 is the top-left of the page, and
x=1.0 and y =1.0 as the bottom-right of t... | 2,270 | 31.442857 | 81 | py |
mmda | mmda-main/src/mmda/types/old/span.old.py | """
Dataclass for doing stuff on token streams of a document
@kylel
"""
from typing import List, Optional, Dict, Union
from mmda.types.boundingbox import BoundingBox
import json
class Span:
def __init__(self, start: int, end: int, id: Optional[int] = None, type: Optional[str] = None,
text: ... | 2,121 | 33.225806 | 98 | py |
mmda | mmda-main/src/mmda/types/old/annotations.old.py | """
Dataclass for representing annotations on documents
@kylel
"""
from typing import List, Optional
import json
from mmda.types.span import Span
from mmda.types.boundingbox import BoundingBox
class Annotation:
def to_json(self):
raise NotImplementedError
def __repr__(self):
return jso... | 1,928 | 24.051948 | 93 | py |
mmda | mmda-main/src/mmda/types/old/document.old.py | """
Dataclass for representing a document and all its constituents
@kylel
"""
from typing import List, Optional, Dict, Tuple, Type
from intervaltree import IntervalTree
from mmda.types.boundingbox import BoundingBox
from mmda.types.annotations import Annotation, SpanAnnotation, BoundingBoxAnnotation
from mmda.typ... | 9,084 | 32.278388 | 119 | py |
mmda | mmda-main/src/mmda/types/old/document_elements.py | """
"""
# TODO[kylel] not sure this class needs to exist; seems extra boilerplate for no benefit
from typing import List, Optional, Dict, Tuple, Type
from abc import abstractmethod
from dataclasses import dataclass, field
@dataclass
class DocumentElement:
"""DocumentElement is the base class for all childr... | 1,889 | 26.794118 | 113 | py |
mmda | mmda-main/src/mmda/utils/stringify.py | """
Utility that converts a List[SpanGroup] into a string
@kylel, @lucas
Prior versions:
- https://github.com/allenai/timo_scim/blob/9fa19cd29cde0e2573e0079da8d776895f4d6caa/scim/predictors/utils.py#L221-L234
-
"""
import logging
from typing import List, Optional
from mmda.types import Document, Span, SpanGroup... | 5,487 | 41.215385 | 147 | py |
mmda | mmda-main/src/mmda/utils/tools.py | from __future__ import annotations
import logging
from collections import defaultdict
from itertools import groupby
import itertools
from typing import List, Dict, Tuple
import numpy as np
from mmda.types.annotation import BoxGroup, SpanGroup
from mmda.types.box import Box
from mmda.types.span import Span
def allo... | 13,197 | 40.373041 | 128 | py |
mmda | mmda-main/src/mmda/utils/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/utils/outline_metadata.py | """
Extract outline (table of contents) metadata from a PDF. Potentially useful for
identifying section headers in the PDF body.
@rauthur
"""
from dataclasses import asdict, dataclass
from io import BytesIO
from typing import Any, Dict, List, Union
import pdfminer.pdfdocument as pd
import pdfminer.pdfpage as pp
imp... | 10,257 | 30.370031 | 88 | py |
mmda | mmda-main/src/mmda/parsers/parser.py | """
Protocol for creating token streams from a document
@kylel, shannons
"""
from abc import abstractmethod
from typing import Protocol
from mmda.types.document import Document
class Parser(Protocol):
@abstractmethod
def parse(self, input_pdf_path: str, **kwargs) -> Document:
"""Given an input PD... | 552 | 20.269231 | 77 | py |
mmda | mmda-main/src/mmda/parsers/symbol_scraper_parser.py | """
Dataclass for creating token streams from a document via SymbolScraper
@kylel
"""
import os
import json
import logging
import math
import re
import subprocess
import tempfile
from collections import defaultdict
from typing import Optional, List, Dict, Tuple
from mmda.types.span import Span
from mmda.types.box ... | 13,725 | 47.673759 | 130 | py |
mmda | mmda-main/src/mmda/parsers/pdfplumber_parser.py | import itertools
import string
from typing import List, Optional, Union, Tuple
import pdfplumber
try:
# pdfplumber >= 0.8.0
import pdfplumber.utils.text as ppu
except:
# pdfplumber <= 0.7.6
import pdfplumber.utils as ppu
from mmda.parsers.parser import Parser
from mmda.types.annotation import SpanGro... | 18,743 | 40.105263 | 144 | py |
mmda | mmda-main/src/mmda/parsers/grobid_parser.py | """
@rauthur, @kylel
"""
import os
import io
import xml.etree.ElementTree as et
from typing import List, Optional, Text
import requests
import tempfile
import json
from mmda.parsers.parser import Parser
from mmda.types.annotation import SpanGroup
from mmda.types.document import Document
from mmda.types.metadata imp... | 3,770 | 28.232558 | 101 | py |
mmda | mmda-main/src/mmda/parsers/__init__.py | from mmda.parsers.pdfplumber_parser import PDFPlumberParser
__all__ = [
'PDFPlumberParser'
] | 97 | 18.6 | 59 | py |
mmda | mmda-main/src/mmda/parsers/grobid_augment_existing_document_parser.py | """
@geli-gel
"""
from grobid_client.grobid_client import GrobidClient
from typing import List, Optional
import os
import xml.etree.ElementTree as et
from mmda.parsers.parser import Parser
from mmda.types import Metadata
from mmda.types.annotation import BoxGroup, Box, SpanGroup
from mmda.types.document import Docu... | 5,682 | 33.23494 | 104 | py |
mmda | mmda-main/src/ai2_internal/api.py | from typing import List, Optional, Type
from pydantic import BaseModel, Extra, Field
from pydantic.fields import ModelField
import mmda.types.annotation as mmda_ann
__all__ = ["BoxGroup", "SpanGroup"]
class Box(BaseModel):
left: float
top: float
width: float
height: float
page: int
@classm... | 4,799 | 27.742515 | 76 | py |
mmda | mmda-main/src/ai2_internal/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/bibentry_predictor_mmda/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 5,941 | 40.552448 | 214 | py |
mmda | mmda-main/src/ai2_internal/bibentry_predictor_mmda/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/bibentry_predictor_mmda/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
from typing import List
from pydantic import BaseModel, BaseSettings, Field
from ai2_internal import api
from mmd... | 5,306 | 35.854167 | 107 | py |
mmda | mmda-main/src/ai2_internal/dwp_heuristic/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 2,347 | 24.802198 | 87 | py |
mmda | mmda-main/src/ai2_internal/dwp_heuristic/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/dwp_heuristic/interface.py | from typing import List
from pydantic import BaseModel, BaseSettings, Field
from ai2_internal.api import SpanGroup
from mmda.predictors.heuristic_predictors.dictionary_word_predictor import (
DictionaryWordPredictor
)
from mmda.types.document import Document
class Instance(BaseModel):
"""
Inference inpu... | 2,309 | 29.8 | 110 | py |
mmda | mmda-main/src/ai2_internal/citation_links/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 4,538 | 31.191489 | 274 | py |
mmda | mmda-main/src/ai2_internal/citation_links/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/citation_links/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
from typing import List, Tuple
from pydantic import BaseModel, BaseSettings, Field
from ai2_internal import api
f... | 3,401 | 34.4375 | 138 | py |
mmda | mmda-main/src/ai2_internal/figure_table_predictors/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 3,639 | 36.525773 | 103 | py |
mmda | mmda-main/src/ai2_internal/figure_table_predictors/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/figure_table_predictors/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
from typing import List
from pydantic import BaseModel, BaseSettings
from mmda.predictors.heuristic_predictors.fi... | 4,263 | 35.135593 | 117 | py |
mmda | mmda-main/src/ai2_internal/vila/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 2,531 | 26.521739 | 93 | py |
mmda | mmda-main/src/ai2_internal/vila/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/vila/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
import logging
from typing import List
import torch
from pydantic import BaseModel, BaseSettings, Field
from ai2_... | 3,302 | 27.721739 | 92 | py |
mmda | mmda-main/src/ai2_internal/bibentry_predictor/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 2,267 | 31.4 | 226 | py |
mmda | mmda-main/src/ai2_internal/bibentry_predictor/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/bibentry_predictor/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
from typing import List, Optional
from pydantic import BaseModel, BaseSettings, Field
from mmda.predictors.hf_pre... | 3,109 | 32.085106 | 89 | py |
mmda | mmda-main/src/ai2_internal/layout_parser/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 1,513 | 24.661017 | 117 | py |
mmda | mmda-main/src/ai2_internal/layout_parser/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/layout_parser/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
import logging
from typing import List
import torch
from pydantic import BaseModel, BaseSettings, Field
from ai2_... | 3,946 | 32.449153 | 85 | py |
mmda | mmda-main/src/ai2_internal/citation_mentions/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
predict_batch(instances: List[Instance]) -> List[Prediction]
"""
impo... | 1,938 | 23.2375 | 101 | py |
mmda | mmda-main/src/ai2_internal/citation_mentions/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/citation_mentions/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
from typing import List
from pydantic import BaseModel, BaseSettings
from ai2_internal import api
from mmda.predi... | 2,867 | 31.590909 | 97 | py |
mmda | mmda-main/src/ai2_internal/bibentry_detection_predictor/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 5,174 | 32.823529 | 118 | py |
mmda | mmda-main/src/ai2_internal/bibentry_detection_predictor/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/bibentry_detection_predictor/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
from typing import List
from pydantic import BaseModel, BaseSettings, Field
from ai2_internal import api
from mmd... | 5,523 | 36.073826 | 170 | py |
mmda | mmda-main/src/ai2_internal/svm_word_predictor/integration_test.py | """
Write integration tests for your model interface code here.
The TestCase class below is supplied a `container`
to each test method. This `container` object is a proxy to the
Dockerized application running your model. It exposes a single method:
```
predict_batch(instances: List[Instance]) -> List[Prediction]
```
... | 2,146 | 25.182927 | 87 | py |
mmda | mmda-main/src/ai2_internal/svm_word_predictor/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/ai2_internal/svm_word_predictor/interface.py | from typing import List
from pydantic import BaseModel, BaseSettings, Field
from ai2_internal.api import SpanGroup
from mmda.predictors.sklearn_predictors.svm_word_predictor import SVMWordPredictor
from mmda.types.document import Document
class Instance(BaseModel):
"""
Inference input for a single paper.
... | 2,335 | 30.567568 | 115 | py |
mmda | mmda-main/tests/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/tests/test_predictors/test_vila_predictors.py | import json
import os
import pathlib
import unittest
from PIL import Image
from mmda.types.document import Document
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.rasterizers.rasterizer import PDF2ImageRasterizer
from mmda.predictors.lp_predictors import LayoutParserPredictor
from mmda.predicto... | 4,777 | 31.951724 | 98 | py |
mmda | mmda-main/tests/test_predictors/test_svm_word_predictor.py | """
Tests for SVM Word Predictor
@kylel
"""
import json
import os
import unittest
from typing import List, Optional, Set
import numpy as np
from mmda.predictors.sklearn_predictors.svm_word_predictor import (
SVMClassifier,
SVMWordPredictor,
)
from mmda.types import Document, Span, SpanGroup
class TestSVMC... | 27,037 | 37.189266 | 88 | py |
mmda | mmda-main/tests/test_predictors/test_bibentry_predictor.py | import unittest
from mmda.predictors.hf_predictors.bibentry_predictor.types import (
BibEntryPredictionWithSpan,
StringWithSpan
)
from mmda.predictors.hf_predictors.bibentry_predictor import utils
from mmda.types.annotation import SpanGroup
from mmda.types.span import Span
class TestBibEntryPredictor(unittes... | 2,922 | 57.46 | 259 | py |
mmda | mmda-main/tests/test_predictors/test_figure_table_predictors.py | import json
import pickle
import unittest
from collections import defaultdict
import pathlib
import pytest
from ai2_internal.api import Relation
from mmda.predictors.heuristic_predictors.figure_table_predictors import FigureTablePredictions
from mmda.types import Document, BoxGroup
from mmda.types.box import Box
from ... | 11,705 | 65.511364 | 113 | py |
mmda | mmda-main/tests/test_predictors/test_section_header_predictor.py | """
Tests for SectionHeaderPredictor
@rauthur
"""
import pathlib
import unittest
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.heuristic_predictors.section_header_predictor import (
SectionHeaderPredictor,
)
from mmda.utils.outline_metadata import PDFMinerOutlineExtractor
cla... | 1,138 | 31.542857 | 80 | py |
mmda | mmda-main/tests/test_predictors/test_section_nesting_predictor.py | """
Tests for SectionNestingPredictor
@rauthur
"""
import pathlib
import unittest
from copy import deepcopy
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.hf_predictors.vila_predictor import IVILAPredictor
from mmda.predictors.lp_predictors import LayoutParserPredictor
from mmda.pre... | 2,846 | 32.892857 | 83 | py |
mmda | mmda-main/tests/test_predictors/test_dictionary_word_predictor.py | """
Tests for DictionaryWordPredictor
@rauthur, @kylel
"""
import tempfile
import unittest
from typing import List, Optional, Set
from mmda.predictors.heuristic_predictors.dictionary_word_predictor import Dictionary
from mmda.predictors import DictionaryWordPredictor
from mmda.types import Document, SpanGroup, Span
... | 7,345 | 34.148325 | 98 | py |
mmda | mmda-main/tests/test_predictors/test.json.py | 0 | 0 | 0 | py | |
mmda | mmda-main/tests/test_predictors/test_span_group_classification_predictor.py | """
@kylel
"""
import unittest
import json
from mmda.types.annotation import Span, SpanGroup
from mmda.types.document import Document
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.hf_predictors.span_group_classification_predictor import (
SpanGroupClassificationPredictor
)
T... | 50,583 | 711.450704 | 48,239 | py |
mmda | mmda-main/tests/test_predictors/test_whitespace_predictor.py | """
@kylel
"""
import unittest
from mmda.predictors import WhitespacePredictor
from mmda.types import Document, SpanGroup, Span
class TestWhitespacePredictor(unittest.TestCase):
def test_predict(self):
# fmt:off
#0 10 20 30 40 50 60 70... | 1,942 | 34.327273 | 98 | py |
mmda | mmda-main/tests/test_types/test_indexers.py | import unittest
from mmda.types import SpanGroup, Span
from mmda.types.indexers import SpanGroupIndexer
class TestSpanGroupIndexer(unittest.TestCase):
def test_overlap_within_single_spangroup_fails_checks(self):
span_groups = [
SpanGroup(
id=1,
spans=[
... | 1,739 | 23.857143 | 85 | py |
mmda | mmda-main/tests/test_types/test_box.py | import unittest
from mmda.types import box as mmda_box
class TestBox(unittest.TestCase):
def setUp(cls) -> None:
cls.box_dict = {'left': 0.2,
'top': 0.09,
'width': 0.095,
'height': 0.017,
'page': 0}
cls... | 571 | 29.105263 | 71 | py |
mmda | mmda-main/tests/test_types/test_document.py | import json
import unittest
import os
from mmda.types.annotation import SpanGroup
from mmda.types.document import Document
from mmda.types.names import MetadataField, SymbolsField
from ai2_internal import api
def resolve(file: str) -> str:
return os.path.join(os.path.dirname(__file__), "../fixtures/types", file... | 4,466 | 39.243243 | 125 | py |
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