metadata

language: de
library_name: sentence-transformers
tags:
  - sentence-similarity
widget:
  - source_sentence: Bebauungspläne, vorhabenbezogene Bebauungspläne (Geltungsbereiche)
    sentences:
      - Fachkräfte für Glücksspielsuchtprävention und -beratung
      - Tagespflege Altenhilfe
      - >-
        Bebauungsplan, Bebauungsplan der Innenentwicklung gem. § 13a BauGB -
        Ortskern Rütenbrock
    example_title: Bebauungsplan
datasets: and-effect/mdk_gov_data_titles_clf
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: musterdatenkatalog_clf
    results:
      - task:
          type: text-classification
        dataset:
          name: and-effect/mdk_gov_data_titles_clf
          type: and-effect/mdk_gov_data_titles_clf
          split: test
          revision: 172e61bb1dd20e43903f4c51e5cbec61ec9ae6e6
        metrics:
          - type: accuracy
            value: 0.6762295081967213
            name: Accuracy 'Bezeichnung'
          - type: precision
            value: 0.5688091249507292
            name: Precision 'Bezeichnung' (macro)
          - type: recall
            value: 0.5981436148510813
            name: Recall 'Bezeichnung' (macro)
          - type: f1
            value: 0.5693466048057273
            name: Recall 'Bezeichnung' (macro)
          - type: accuracy
            value: 0.8934426229508197
            name: Accuracy 'Thema'
          - type: precision
            value: 0.9258716898716898
            name: Precision 'Thema' (macro)
          - type: recall
            value: 0.8669105248121641
            name: Recall 'Thema' (macro)
          - type: f1
            value: 0.8632335412054082
            name: Recall 'Thema' (macro)
pipeline_tag: sentence-similarity
inferece: false

Model Card for Musterdatenkatalog Classifier

Model Details

Model Description

This model is based on bert-base-german-cased and fine-tuned on and-effect/mdk_gov_data_titles_clf. This model reaches an accuracy of XY on the test set and XY on the validation set

Developed by: and-effect
Shared by: [More Information Needed]
Model type: Text Classification
Language(s) (NLP): de
License: [More Information Needed]
Finetuned from model: "bert-base-german-case. For more information one the model check on this model card"

Model Sources

Repository: [More Information Needed]
Paper: [More Information Needed]
Demo: [More Information Needed]

Direct Use

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Get Started with Sentence Transformers

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

Get Started with HuggingFace Transformers

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Downstream Use

The model is intended to classify open source dataset titles from german municipalities. More information on the Taxonomy (classification categories) and the Project can be found on XY.

[More Information Needed on downstream_use_demo]

Bias, Risks, and Limitations

The model has some limititations. The model has some limitations in terms of the downstream task. \n 1. Distribution of classes: The dataset trained on is small, but at the same time the number of classes is very high. Thus, for some classes there are only a few examples (more information about the class distribution of the training data can be found here). Consequently, the performance for smaller classes may not be as good as for the majority classes. Accordingly, the evaluation is also limited. \n 2. Systematic problems: some subjects could not be correctly classified systematically. One example is the embedding of titles containing 'Corona'. In none of the evaluation cases could the titles be embedded in such a way that they corresponded to their true names. Another systematic example is the embedding and classification of titles related to 'migration'. \n 3. Generalization of the model: by using semantic search, the model is able to classify titles into new categories that have not been trained, but the model is not tuned for this and therefore the performance of the model for unseen classes is likely to be limited.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

You can find all information about the training data here. For the Fine Tuning we used the revision 172e61bb1dd20e43903f4c51e5cbec61ec9ae6e6 of the data, since the performance was better with this previous version of the data.

Training Procedure

Preprocessing

This section describes the generating of the input data for the model. More information on the preprocessing of the data itself can be found here

The model is fine tuned with similar and dissimilar pairs. Similar pairs are built with all titles and their true label. Dissimilar pairs defined as pairs of title and all labels, except the true label. Since the combinations of dissimilar is much higher, a sample of two pairs per title is selected.

pairs	size
train_similar_pairs	1964
train_unsimilar_pairs	982
test_similar_pairs	498
test_unsimilar_pairs	249

Training Parameter

The model was trained with the parameters:

DataLoader: torch.utils.data.dataloader.DataLoader

Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Hyperparameter:

{
    "epochs": 3,
    "warmup_steps": 100,
}

Speeds, Sizes, Times

[More Information Needed]

Evaluation

All metrices express the models ability to classify dataset titles from GOVDATA into the taxonomy described here. For more information see VERLINKUNG MDK Projekt.

Testing Data, Factors & Metrics

Testing Data

The evaluation data can be found here. Since the model is trained on revision 172e61bb1dd20e43903f4c51e5cbec61ec9ae6e6 for evaluation, the evaluation metrics rely on the same revision.

Metrics

The model performance is tested with fours metrices. Accuracy, Precision, Recall and F1 Score. A lot of classes were not predicted and are thus set to zero for the calculation of precision, recall and f1 score. For these metrices the additional calucations were performed exluding classes with less than two predictions for the level 'Bezeichnung' (see in table results 'Bezeichnung II'. Although intepretation of these results should be interpreted with caution, because they do not represent all classes.

Results

task	acccuracy	precision (macro)	recall (macro)	f1 (macro)
Test dataset 'Bezeichnung' I	0.6762295081967213	0.5688091249507292	0.5981436148510813	0.5693466048057273
Test dataset 'Thema' I	0.8934426229508197	0.9258716898716898	0.8669105248121641	0.8632335412054082
Test dataset 'Bezeichnung' II	0.6762295081967213	0.5598761408083442	0.7875393612235718	0.6306226331603018
Validation dataset 'Bezeichnung' I	0.5445544554455446	0.41787439613526567	0.39929183135704877	0.4010173484686228
Validation dataset 'Thema' I	0.801980198019802	0.6433080808080808	0.7039711632453568	0.6591710279769981
Validation dataset 'Bezeichnung' II	0.5445544554455446	0.6018518518518517	0.6278409090909091	0.6066776135741653

and-effect
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musterdatenkatalog_clf