sentence-embedding-evaluation-german 0.1.1

Sentence embedding evaluation for German

sentence-embedding-evaluation-german

Sentence embedding evaluation for German.

This library is inspired by SentEval but focuses on German language downstream tasks.

Downstream tasks

task	type	properties	#train	#test	target	info
TOXIC	👿 toxic comments	facebook comments	3244	944	binary {0,1}	GermEval 2021, comments subtask 1, 📁 📖
ENGAGE	🤗 engaging comments	facebook comments	3244	944	binary {0,1}	GermEval 2021, comments subtask 2, 📁 📖
FCLAIM	☝️ fact-claiming comments	facebook comments	3244	944	binary {0,1}	GermEval 2021, comments subtask 3, 📁 📖
VMWE	verbal idioms	newspaper	6652	1447	binary (figuratively, literally)	GermEval 2021, verbal idioms, 📁 📖
OL19-A	👿 offensive language	tweets	3980	3031	binary {0,1}	GermEval 2018, 📁 📖
OL19-B	👿 offensive language, fine-grained	tweets	3980	3031	4 catg. (profanity, insult, abuse, oth.)	GermEval 2018, 📁 📖
OL19-C	👿 explicit vs. implicit offense	tweets	1921	930	binary (explicit, implicit)	GermEval 2018, 📁 📖
OL18-A	👿 offensive language	tweets	5009	3398	binary {0,1}	GermEval 2018, 📁
OL18-B	👿 offensive language, fine-grained	tweets	5009	3398	4 catg. (profanity, insult, abuse, oth.)	GermEval 2018, 📁
ABSD-1	🛤️ relevance classification	'Deutsche Bahn' customer feedback, lang:de-DE	19432	2555	binary	GermEval 2017, 📁
ABSD-2	🛤️ Sentiment analysis	'Deutsche Bahn' customer feedback, lang:de-DE	19432	2555	3 catg. (pos., neg., neutral)	GermEval 2017, 📁
ABSD-3	🛤️ aspect categories	'Deutsche Bahn' customer feedback, lang:de-DE	19432	2555	20 catg.	GermEval 2017, 📁
MIO-S	Sentiment analysis	'Der Standard' newspaper article web comments, lang:de-AT	1799	1800	3 catg.	One Million Posts Corpus, 📁
MIO-O	off-topic comments	'Der Standard' newspaper article web comments, lang:de-AT	1799	1800	binary	One Million Posts Corpus, 📁
MIO-I	inappropriate comments	'Der Standard' newspaper article web comments, lang:de-AT	1799	1800	binary	One Million Posts Corpus, 📁
MIO-D	discriminating comments	'Der Standard' newspaper article web comments, lang:de-AT	1799	1800	binary	One Million Posts Corpus, 📁
MIO-F	feedback comments	'Der Standard' newspaper article web comments, lang:de-AT	3019	3019	binary	One Million Posts Corpus, 📁
MIO-P	personal story comments	'Der Standard' newspaper article web comments, lang:de-AT	4668	4668	binary	One Million Posts Corpus, 📁
MIO-A	argumentative comments	'Der Standard' newspaper article web comments, lang:de-AT	1799	1800	binary	One Million Posts Corpus, 📁
SBCH-L	Swiss German detection	'chatmania' app comments, lang:gsw	748	748	binary	SB-CH Corpus, 📁
SBCH-S	Sentiment analysis	'chatmania' app comments, only comments labelled as Swiss German are included, lang:gsw	394	394	3 catg.	SB-CH Corpus, 📁
ARCHI	Swiss German Dialect Classification	lang:gsw	18809	4743	4 catg.	ArchiMob, 📁 📖
LSDC	Lower Saxon Dialect Classification	lang:nds	74140	8602	14 catg.	LSDC, 📁 📖

Download datasets

bash download-datasets.sh

Usage example

from typing import List
import sentence_embedding_evaluation_german as seeg
import torch

# (1) Instantiate your Embedding model
emb_dim = 512
vocab_sz = 128
emb = torch.randn((vocab_sz, emb_dim), requires_grad=False)
emb = torch.nn.Embedding.from_pretrained(emb)
assert emb.weight.requires_grad == False

# (2) Specify the preprocessing
def preprocesser(batch: List[str], params: dict=None) -> List[List[float]]:
    """ Specify your embedding or pretrained encoder here
    Paramters:
    ----------
    params : dict
        The params dictionary
    batch : List[str]
        A list of sentence as string
    Returns:
    --------
    List[List[float]]
        A list of embedding vectors
    """
    features = []
    for sent in batch:
        try:
            ids = torch.tensor([ord(c) % 128 for c in sent])
        except:
            print(sent)
        h = emb(ids)
        features.append(h.mean(axis=0))
    features = torch.stack(features, dim=0)
    return features

# (3) Training settings
params = {
    'datafolder': './datasets',
    'bias': True,
    'balanced': True,
    'batch_size': 128, 
    'num_epochs': 500,
    # 'early_stopping': True,
    # 'split_ratio': 0.2,  # if early_stopping=True
    # 'patience': 5,  # if early_stopping=True
}

# (4) Specify downstream tasks
downstream_tasks = ['FCLAIM', 'VMWE', 'OL19-C', 'ABSD-2', 'MIO-P', 'ARCHI', 'LSDC']

# (5) Run experiments
results = seeg.evaluate(downstream_tasks, preprocesser, **params)

Appendix

Installation

The sentence-embedding-evaluation-german git repo is available as PyPi package

pip install sentence-embedding-evaluation-german
pip install git+ssh://[e-mail]/ulf1/sentence-embedding-evaluation-german.git

Install a virtual environment

python3.7 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir
pip install -r requirements-dev.txt --no-cache-dir
pip install -r requirements-demo.txt --no-cache-dir

(If your git repo is stored in a folder with whitespaces, then don't use the subfolder .venv. Use an absolute path without whitespaces.)

Python commands

• Jupyter for the examples: jupyter lab
• Check syntax: flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')

Publish

pandoc README.md --from markdown --to rst -s -o README.rst
python setup.py sdist 
twine upload -r pypi dist/*

Clean up

find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.