Debatenet 2 Cat

🚀 nblokker/debatenet-2-cat

This model, based on sentence-transformers, maps sentences and paragraphs to a 768-dimensional dense vector space. It can be applied to tasks such as clustering and semantic search. It's particularly useful for estimating similarities between sentences related to migration demands and propositions. For more details and potential use cases, check out this blog post.

🚀 Quick Start

This is a sentence-transformers model that can map sentences and paragraphs to a 768-dimensional dense vector space, which can be used for tasks like clustering or semantic search. It can estimate the similarities between sentences containing migration-related demands and propositions.

✨ Features

• Multilingual Support: Supports multiple languages, including German and English.
• Fine-Tuned Model: Based on the sentence-transformers_paraphrase-multilingual-mpnet-base-v2 model, fine-tuned for specific tasks.
• Versatile Applications: Can be used for clustering, semantic search, and estimating sentence similarities.

📦 Installation

To use this model, you need to install sentence-transformers:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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

model = SentenceTransformer('nblokker/debatenet-2-cat')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without sentence-transformers, you can use the model as follows. First, pass your input through the transformer model, then apply the appropriate 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('nblokker/debatenet-2-cat')
model = AutoModel.from_pretrained('nblokker/debatenet-2-cat')

# 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)

📚 Documentation

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 38 with parameters:

{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss: sentence_transformers.losses.BatchHardSoftMarginTripletLoss.BatchHardSoftMarginTripletLoss

Parameters of the fit()-Method:

{
    "epochs": 15,
    "evaluation_steps": 120.5,
    "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 120.5,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

🔧 Technical Details

This repository contains a fine-tuned version of the sentence-transformers_paraphrase-multilingual-mpnet-base-v2 model. The original model was created by Nils Reimers and Iryna Gurevych and is available on Hugging Face.

📄 License

The fine-tuned parts of this model are released under the MIT License. See the LICENSE file for more details. The original sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model remains under its original Apache 2.0 License.

Citing & Authors

@preprint{blokker2023,
  author = {Blokker, Nico and Blessing, Andre and Dayanik, Erenay and Kuhn, Jonas and Padó, Sebastian and Lapesa, Gabriella},
  note = {To appear in \textit{Language Resources and Evaluation}},
  title = {Between welcome culture and border fence: The {E}uropean refugee crisis in {G}erman newspaper reports},
  url = {https://arxiv.org/abs/2111.10142},
  year = 2023
}

@inproceedings{lapesa2020,
  abstract = {DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015. The building block of our annotation is the political science notion of a claim, i.e., a statement made by a political actor (a politician, a party, or a group of citizens) that a specific action should be taken (e.g., vacant flats should be assigned to refugees). We identify claims in newspaper articles, assign them to actors and fine-grained categories and annotate their polarity and date. The aim of this paper is two-fold: first, we release the full DEbateNet-mig15 corpus and document it by means of a quantitative and qualitative analysis; second, we demonstrate its application in a discourse network analysis framework, which enables us to capture the temporal dynamics of the political debate.},
  address = {Online},
  author = {Lapesa, Gabriella and Blessing, Andre and Blokker, Nico and Dayanik, Erenay and Haunss, Sebastian and Kuhn, Jonas and Padó, Sebastian},
  booktitle = {Proceedings of LREC},
  pages = {919--927},
  title = {{DEbateNet-mig15}: {T}racing the 2015 Immigration Debate in {G}ermany Over Time},
  url = {https://www.aclweb.org/anthology/2020.lrec-1.115},
  year = 2020
}

Acknowledgments

This model is based on the sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}

• Original model URL: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
• License: Apache 2.0