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healthsea's Issues

ValueError: [E853] Unsupported component factory name 'healthsea.aggregation.v1'. The character '.' is not permitted in factory names.

These are the errors I get when I run this code:

import spacy

nlp = spacy.load("en_healthsea")
doc = nlp("This is great for joint pain")
print(doc._.health_effects)

These errors happen when I install spaCy 3.4.4

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
en-healthsea 0.0.1 requires spacy<3.3.0,>=3.2.0, but you have spacy 3.4.4 which is incompatible.
Traceback (most recent call last):
  File "/home/my_pc/scispaCy/spaCy lessons/Entity_recognition.py", line 24, in <module>
    nlp = spacy.load("en_healthsea")
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/__init__.py", line 54, in load
    return util.load_model(
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 432, in load_model
    return load_model_from_package(name, **kwargs)  # type: ignore[arg-type]
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 467, in load_model_from_package
    cls = importlib.import_module(name)
  File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/vasya/.local/lib/python3.10/site-packages/en_healthsea/__init__.py", line 4, in <module>
    from . import clause_aggregation
  File "/home/vasya/.local/lib/python3.10/site-packages/en_healthsea/clause_aggregation.py", line 6, in <module>
    @Language.factory("healthsea.aggregation.v1")
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/language.py", line 470, in factory
    raise ValueError(Errors.E853.format(name=name))
ValueError: [E853] Unsupported component factory name 'healthsea.aggregation.v1'. The character '.' is not permitted in factory names.

These errors happen when I install spaCy 3.2.0

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
spacy-transformers 1.1.9 requires spacy<4.0.0,>=3.4.0, but you have spacy 3.2.0 which is incompatible.
en-core-web-sm 3.4.1 requires spacy<3.5.0,>=3.4.0, but you have spacy 3.2.0 which is incompatible.
en-core-web-md 3.4.1 requires spacy<3.5.0,>=3.4.0, but you have spacy 3.2.0 which is incompatible.
en-core-web-lg 3.4.1 requires spacy<3.5.0,>=3.4.0, but you have spacy 3.2.0 which is incompatible.
Traceback (most recent call last):
  File "/home/my_pc/scispaCy/spaCy lessons/Entity_recognition.py", line 24, in <module>
    nlp = spacy.load("en_healthsea")
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/__init__.py", line 51, in load
    return util.load_model(
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 420, in load_model
    return load_model_from_package(name, **kwargs)  # type: ignore[arg-type]
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 453, in load_model_from_package
    return cls.load(vocab=vocab, disable=disable, exclude=exclude, config=config)  # type: ignore[attr-defined]
  File "/home/vasya/.local/lib/python3.10/site-packages/en_healthsea/__init__.py", line 14, in load
    return load_model_from_init_py(__file__, **overrides)
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 615, in load_model_from_init_py
    return load_model_from_path(
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 488, in load_model_from_path
    nlp = load_model_from_config(config, vocab=vocab, disable=disable, exclude=exclude)
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/util.py", line 525, in load_model_from_config
    nlp = lang_cls.from_config(
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/language.py", line 1755, in from_config
    nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer, meta=meta)
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy/language.py", line 162, in __init__
    util.registry._entry_point_factories.get_all()
  File "/home/vasya/.local/lib/python3.10/site-packages/catalogue/__init__.py", line 119, in get_all
    result.update(self.get_entry_points())
  File "/home/vasya/.local/lib/python3.10/site-packages/catalogue/__init__.py", line 134, in get_entry_points
    result[entry_point.name] = entry_point.load()
  File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 171, in load
    module = import_module(match.group('module'))
  File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy_transformers/__init__.py", line 1, in <module>
    from . import architectures
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy_transformers/architectures.py", line 6, in <module>
    from .layers import TransformerModel, TransformerListener
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy_transformers/layers/__init__.py", line 1, in <module>
    from .listener import TransformerListener
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy_transformers/layers/listener.py", line 4, in <module>
    from ..data_classes import TransformerData
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy_transformers/data_classes.py", line 12, in <module>
    from .util import transpose_list
  File "/home/vasya/.local/lib/python3.10/site-packages/spacy_transformers/util.py", line 9, in <module>
    from thinc.api import get_torch_default_device
ImportError: cannot import name 'get_torch_default_device' from 'thinc.api' (/home/vasya/.local/lib/python3.10/site-packages/thinc/api.py)

Minor change in the code

Hi!I noticed that in the following line of code in the preprocess_clausecat.py file, at line 61 in the for loop while splitting the dataset into train and test set

for label in label_dict:
        split = int(len(label_dict[label]) * eval_split)
        train += label_dict[label][split:]
        dev += label_dict[label][:split]
        checksum += len(label_dict[label])
        table_data.append(
            (
                label,
                len(label_dict[label]),
                len(label_dict[label][split:]),
                len(label_dict[label][:split]),
            )
        )

the train and dev assignment statements need to be interchanged. As per the existing assignment, The train set has fewer samples than the dev set. Shouldn't it be the other way round?
Something like this?

train += label_dict[label][:split]
dev += label_dict[label][split:]

Case with 2 conditions mix reviews

Sample input: Yam is great for joint pain but bad for liver disease
Code below:

`import spacy

nlp = spacy.load("en_healthsea")
doc = nlp("Yam is great for joint pain but bad for liver disease")

print(doc._.clauses)
print(doc._.health_effects)`

Output below:

[{'split_indices': (0, 11), 'has_ent': True, 'ent_indices': (4, 6), 'blinder': '_CONDITION_', 'ent_name': 'joint pain', 'cats': {'POSITIVE': 0.9715917110443115, 'NEUTRAL': 0.004003751091659069, 'NEGATIVE': 0.01367472019046545, 'ANAMNESIS': 0.010729866102337837}, 'prediction_text': ['Yam', 'is', 'great', 'for', '_CONDITION_', 'but', 'bad', 'for', 'liver', 'disease']}, {'split_indices': (0, 11), 'has_ent': True, 'ent_indices': (9, 11), 'blinder': '_CONDITION_', 'ent_name': 'liver disease', 'cats': {'POSITIVE': 0.9650901556015015, 'NEUTRAL': 0.004530671052634716, 'NEGATIVE': 0.023644359782338142, 'ANAMNESIS': 0.0067347269505262375}, 'prediction_text': ['Yam', 'is', 'great', 'for', 'joint', 'pain', 'but', 'bad', 'for', '_CONDITION_']}]

Expected 1 positive and another negative. Actually found 2 positive sentiments.

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