metadata

language: de
library_name: sentence_transformers
tags:
  - text-classification
model-index:
  - name: and-effect/musterdatenkatalog_clf
    results:
      - task:
          type: text-classification
        dataset:
          name: mdk_gov_data_titles_clf
          type: and-effect/mdk_gov_data_titles_clf
        metrics:
          - type: Accuracy (Bezeichnung)
            value: 0.7
          - type: Precision macro (Bezeichnung)
            value: 0.5

Model Card for Model ID

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 and accuracy of XY on the test set and XY on the validation set

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

Model Sources [optional]

Repository: XY git hub repo?
Paper [optional]: XY and-effect papers?
Demo [optional]: XY Spaces?

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. For more information see Github Repo + Spaces

Bias, Risks, and Limitations

The model has some limititations. The model has some limitations in terms of the downstream task.

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.
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'.
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

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Training Procedure [optional]

Preprocessing

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Speeds, Sizes, Times

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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