davidberenstein1957 / crosslingual-coreference

A multi-lingual approach to AllenNLP CoReference Resolution along with a wrapper for spaCy.

Hi there 👋

From failing to study medicine ➡️ BSc industrial engineer ➡️ MSc computer scientist.
Life can be strange, so better enjoy it.
I´m sure I do by: 👨🏽‍🍳 Cooking, 👨🏽‍💻 Coding, 🏆 Committing.

Conference slides 📖

• 🧼 From GPU-poor to data-rich - data quality practices for LLM fine-tuning
• Deeplearning.ai LLM workshop - get started with Argilla for human- and distilabel for AI feedback
• NLP Healthcare Summit 2023 - Smart Shortcuts for Bootstrapping a Healthcare NER Project
• Anyscale Ray Europe Meetup - Smart shortcuts for Bootstrapping a Text Classification project

employers 👨🏽‍💻

• Argilla(2022-current) - data annotation and monitoring for enterprise NLP
• Pandora Intelligence(2020-2022) - an independent intelligence company, specialized in security risks

open source ⭐️

maintainer 🤓

• concise-concepts - a word similarity approach to few-shot NER
• fast-sentence-transformers - wrapper for ONNX speed enhanced sentence-transformers
• classy-classification - a quick and dirty few-shot text classification solution
• crosslingual-coreference - a multi-lingual CoRef resolver using cross-lingual training
• adept-augmentations - a Python library aimed at dissecting and augmenting NER training data
• spacy-setfit - a Python library aimed to facilitate easy SetFit usage in spaCy

contributions 🫱🏾‍🫲🏼

• spaCy - several additions to the spacy-universe
  • spanmarker - added .pipe() method to spaCy integration
  • spacy-dbpedia-spotlight - added a batch processing functionality
  • spacy-fishing - added a batch processing functionality + bug fixes
  • spacy-opentapioca - added a batch processing functionality
• streamlit-url-fragment - resolved Python versioning issues
• allennlp-models - added a batch processing functionality
• mutate - resolved Python versioning issues and added PyPI support
• rebel - added a batch processing functionality
• trl - updated RLHF documentation for PPOTrainer

volunteering 🌍

• Bonfari - small to medium sustainable scale projects in Gambia 🇬🇲
• 510 red-cross - occasional projects to improve humanitarian aid with data

Contacts

crosslingual-coreference's Issues

How to cite this repository?

Is there publication for this work?

Retrieving cluster heads without replacing corefs

I am interested in being able to extract the cluster heads with something like doc._.coref_cluster_heads to get the cluster heads without getting the reconstituted text. It could be a separate function that also acts as input into replace_corefs potentially.

Which language model is using for minilm

I am using the following code snippet for coreference resolution

predictor = Predictor(language="en_core_web_sm", device=-1, model_name="minilm")

While checking the below source code,

"minilm": {
        "url": (
            "https://storage.googleapis.com/pandora-intelligence/models/crosslingual-coreference/minilm/model.tar.gz"
        ),
        "f1_score_ontonotes": 74,
        "file_extension": ".tar.gz",
    },

it seems that the language model using here is https://storage.googleapis.com/pandora-intelligence/models/crosslingual-coreference/minilm/model.tar.gz

Is this the same one that I can see in https://huggingface.co/models like
https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384/tree/main
or any other huggingface model?

Comparatively high initial prediction time for first predict() hit

I am using minilm model with language 'en_core_web_sm'.
While comparing the prediction time, i.e., predictor.predict(text), the prediction time for first hit is always a bit high than the following hits.
Suppose after creating a predictor object, I call predict as follows:

predictor.predict(text) ---> first call
predictor.predict(text) ---> second call
predictor.predict(text) ---> third call

Time taken for the first call is comparatively a bit higher(.2 sec) than the next prediction calls(.05 sec).
Could you please help me understand why this initial hit takes a bit high prediction time?

support for pro-drop languages [I need a Spanish/Italian Coref dataset]

Why does this package need to install google cloud auth, storage, api etc?

Hi,

after installing the library I saw google-api-core-2.10.1 google-auth-2.12.0 google-cloud-core-2.3.2 google-cloud-storage-1.44.0 have been installed as well. In fact these packages can be found in the poetry.lock file.

Is there a reason (I don't get) why this library needs these packages?

Thanks

crosslingual_coreference is not installing on VM , jsonnet is the package giving the error

Local run

Is there a way to run locally?
First, download all the data locally, and then run it locally via docker

Error when using coref as a spaCy pipeline

Hi all,
while trying to run a spacy test

import spacy
import crosslingual_coreference

text = """
    Do not forget about Momofuku Ando!
    He created instant noodles in Osaka.
    At that location, Nissin was founded.
    Many students survived by eating these noodles, but they don't even know him."""

# use any model that has internal spacy embeddings
nlp = spacy.load('en_core_web_sm')
nlp.add_pipe(
    "xx_coref", config={"chunk_size": 2500, "chunk_overlap": 2, "device": 0})

doc = nlp(text)

print(doc._.coref_clusters)
print(doc._.resolved_text)

I encountered the following issue:

[nltk_data] Downloading package omw-1.4 to
[nltk_data]     /home/user/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!
Traceback (most recent call last):
  File "/home/user/test_coref/test.py", line 12, in <module>
    nlp.add_pipe(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/spacy/language.py", line 792, in add_pipe
    pipe_component = self.create_pipe(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/spacy/language.py", line 674, in create_pipe
    resolved = registry.resolve(cfg, validate=validate)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/thinc/config.py", line 746, in resolve
    resolved, _ = cls._make(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/thinc/config.py", line 795, in _make
    filled, _, resolved = cls._fill(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/thinc/config.py", line 867, in _fill
    getter_result = getter(*args, **kwargs)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/crosslingual_coreference/__init__.py", line 33, in make_crosslingual_coreference
    return SpacyPredictor(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/crosslingual_coreference/CrossLingualPredictorSpacy.py", line 18, in __init__
    super().__init__(language, device, model_name, chunk_size, chunk_overlap)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/crosslingual_coreference/CrossLingualPredictor.py", line 55, in __init__
    self.set_coref_model()
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/crosslingual_coreference/CrossLingualPredictor.py", line 85, in set_coref_model
    self.predictor = Predictor.from_path(self.filename, language=self.language, cuda_device=self.device)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/predictors/predictor.py", line 366, in from_path
    load_archive(archive_path, cuda_device=cuda_device, overrides=overrides),
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/models/archival.py", line 232, in load_archive
    dataset_reader, validation_dataset_reader = _load_dataset_readers(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/models/archival.py", line 268, in _load_dataset_readers
    dataset_reader = DatasetReader.from_params(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 604, in from_params
    return retyped_subclass.from_params(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 636, in from_params
    kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 206, in create_kwargs
    constructed_arg = pop_and_construct_arg(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 314, in pop_and_construct_arg
    return construct_arg(class_name, name, popped_params, annotation, default, **extras)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 394, in construct_arg
    value_dict[key] = construct_arg(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 348, in construct_arg
    result = annotation.from_params(params=popped_params, **subextras)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 604, in from_params
    return retyped_subclass.from_params(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/from_params.py", line 638, in from_params
    return constructor_to_call(**kwargs)  # type: ignore
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/data/token_indexers/pretrained_transformer_mismatched_indexer.py", line 58, in __init__
    self._matched_indexer = PretrainedTransformerIndexer(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/data/token_indexers/pretrained_transformer_indexer.py", line 56, in __init__
    self._allennlp_tokenizer = PretrainedTransformerTokenizer(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/data/tokenizers/pretrained_transformer_tokenizer.py", line 72, in __init__
    self.tokenizer = cached_transformers.get_tokenizer(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/allennlp/common/cached_transformers.py", line 204, in get_tokenizer
    tokenizer = transformers.AutoTokenizer.from_pretrained(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/transformers/models/auto/tokenization_auto.py", line 546, in from_pretrained
    return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/transformers/tokenization_utils_base.py", line 1788, in from_pretrained
    return cls._from_pretrained(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/transformers/tokenization_utils_base.py", line 1923, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py", line 140, in __init__
    super().__init__(
  File "/home/user/test_coref/.venv/lib/python3.9/site-packages/transformers/tokenization_utils_fast.py", line 110, in __init__
    fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
Exception: EOF while parsing a list at line 1 column 4920583

Here's what I have installed (pulled by poetry add crosslingual-coreference or pip install crosslingual-coreference):

(.venv) user@host$ pip freeze
aiohttp==3.8.1
aiosignal==1.2.0
allennlp==2.9.3
allennlp-models==2.9.3
async-timeout==4.0.2
attrs==21.4.0
base58==2.1.1
blis==0.7.7
boto3==1.23.5
botocore==1.26.5
cached-path==1.1.2
cachetools==5.1.0
catalogue==2.0.7
certifi==2022.5.18.1
charset-normalizer==2.0.12
click==8.0.4
conllu==4.4.1
crosslingual-coreference==0.2.4
cymem==2.0.6
datasets==2.2.1
dill==0.3.5.1
docker-pycreds==0.4.0
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl
en-core-web-trf @ https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.2.0/en_core_web_trf-3.2.0-py3-none-any.whl
fairscale==0.4.6
filelock==3.6.0
frozenlist==1.3.0
fsspec==2022.5.0
ftfy==6.1.1
gitdb==4.0.9
GitPython==3.1.27
google-api-core==2.8.0
google-auth==2.6.6
google-cloud-core==2.3.0
google-cloud-storage==2.3.0
google-crc32c==1.3.0
google-resumable-media==2.3.3
googleapis-common-protos==1.56.1
h5py==3.6.0
huggingface-hub==0.5.1
idna==3.3
iniconfig==1.1.1
Jinja2==3.1.2
jmespath==1.0.0
joblib==1.1.0
jsonnet==0.18.0
langcodes==3.3.0
lmdb==1.3.0
MarkupSafe==2.1.1
more-itertools==8.13.0
multidict==6.0.2
multiprocess==0.70.12.2
murmurhash==1.0.7
nltk==3.7
numpy==1.22.4
packaging==21.3
pandas==1.4.2
pathtools==0.1.2
pathy==0.6.1
Pillow==9.1.1
pluggy==1.0.0
preshed==3.0.6
promise==2.3
protobuf==3.20.1
psutil==5.9.1
py==1.11.0
py-rouge==1.1
pyarrow==8.0.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pydantic==1.8.2
pyparsing==3.0.9
pytest==7.1.2
python-dateutil==2.8.2
pytz==2022.1
PyYAML==6.0
regex==2022.4.24
requests==2.27.1
responses==0.18.0
rsa==4.8
s3transfer==0.5.2
sacremoses==0.0.53
scikit-learn==1.1.1
scipy==1.6.1
sentence-transformers==2.2.0
sentencepiece==0.1.96
sentry-sdk==1.5.12
setproctitle==1.2.3
shortuuid==1.0.9
six==1.16.0
smart-open==5.2.1
smmap==5.0.0
spacy==3.2.4
spacy-alignments==0.8.5
spacy-legacy==3.0.9
spacy-loggers==1.0.2
spacy-sentence-bert==0.1.2
spacy-transformers==1.1.5
srsly==2.4.3
tensorboardX==2.5
termcolor==1.1.0
thinc==8.0.16
threadpoolctl==3.1.0
tokenizers==0.12.1
tomli==2.0.1
torch==1.10.2
torchaudio==0.10.2
torchvision==0.11.3
tqdm==4.64.0
transformers==4.17.0
typer==0.4.1
typing-extensions==4.2.0
urllib3==1.26.9
wandb==0.12.16
wasabi==0.9.1
wcwidth==0.2.5
word2number==1.1
xxhash==3.0.0
yarl==1.7.2

Do you have any recommendations?
Is there an installation step missing?

Thanks in advance!

Support for Spacy 3.4.0

Hi, I would like to use this nice package for Dutch language models that only work with Spacy 3.4.0+. How difficult would it be to support spacy 3.4.0?

Unable to download info_xml

spaCy issues and suggestions

@martin-kirilov It might be worth looking into including batching + training a model for Spanish/Italian. See this issue from spaCy.

• batching
• empty cluster issue (resolved)
• additional model pro-drop languages

feat: look into ONNX enhanched transformer embeddings

Creating embeddings roughly takes 50% of the inference time. allennlp/modules/token_embedders/pretrained_transformer_embedder.py hold the logic for creating these embeddings. Make sure we can call them in a faster way.

HTTPSConnectionPool(host='storage.googleapis.com', port=443): Max retries exceeded with url: /pandora-intelligence/models/crosslingual-coreference/minilm/model.tar.gz

Python 3.8.13
Spacy - 3.1.0
en_core_web_sm-3.1.0
crosslingual_coreference - 0.2.8

requests.exceptions.SSLError: HTTPSConnectionPool(host='storage.googleapis.com', port=443): Max retries exceeded with url: /pandora-intelligence/models/crosslingual-coreference/minilm/model.tar.gz (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)')))

Is it possible to load own model locally

@davidberenstein1957 @dvsrepo @martin-kirilov @DavidFromPandora is there any possible ways to use own model locally.

[Errno 101] Network is unreachable

Hello, when I try to run the code below

predictor = Predictor(
    language="en_core_web_sm", device=1, model_name="info_xlm"
)

I get the following error:

ConnectionError: HTTPSConnectionPool(host='cdn-lfs.huggingface.co', port=443): Max retries exceeded with url: /microsoft/infoxlm-base/cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff90cba1a00>: Failed to establish a new connection: [Errno 101] Network is unreachable'))

Is this url still valid and what should I use instead?

detach codebase with unsupported `allennlp` and `allennlp-models`

@Masboes @rdeheer2 This is a typical example of a high prior task, which I will not be able to pick up easily. Similarly, if not done, this will break potential to update the package in the long run, due to the fact that allennlp is no longer supported.

installation failed: 'notebook' has no attribute 'nbextensions'

I tried to install it in a venv:
(.spaCy) PS C:\Users\joajo\Documents> pip --version
pip 23.2.1 from C:\Users\joajo\Documents.spaCy\Lib\site-packages\pip (python 3.11)

 AttributeError: module 'notebook' has no attribute 'nbextensions'
  [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

Visualization code

Please add displacy module to visualise

Installation failed: Failed building wheel for tokenizers

ERROR: Failed building wheel for tokenizers
Failed to build tokenizers
ERROR: Could not build wheels for tokenizers, which is required to install pyproject.toml-based projects

Using Python 3.9, Python 3.12

How do we character ranges of the clusters

Right now, when we call predictor.predict() we get the clusters as a list of lists, and the cluster heads along with their token indices. Is it possible to :

• Get the cluster heads as character ranges? Meaning that 'cluster_heads': {'Momofuku Ando': [4, 5], 'Osaka': [12, 12], 'instant noodles':
  [9, 10], 'Many students': [22, 23], 'Nissin': [18, 18]}}, instead of the token/word indices like [4,5], [12,12], etc. can we get the character ranges
• Alternatively, can we get a separate variable that maps the token indices to tokens? Something like ['Do', 'not', 'forget', 'about'....] .
  I tried looking at how the text is tokenized but couldn't exactly get that from the code. Basically for my application I need to check whether a coreference appears in a particular character range, and would like to do that accurately (with the best way to do that being using the character range)