初始化项目，由ModelHub XC社区提供模型
Model: nblokker/debatenet-2-cat Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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license: [mit, apache-2.0]
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language:
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- multilingual
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- de
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- en
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---
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# nblokker/debatenet-2-cat
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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This model may be used to estimate the similarities between sentences containing migration-related demands and propositions. Check out this [blog post](https://nicoblokker.github.io/PTaD/posts/qca_meets_sbert/dna_meets_sbert.html) for more information and potential use cases.
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# Fine-Tuned on `sentence-transformers_paraphrase-multilingual-mpnet-base-v2` Model
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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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2).
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('nblokker/debatenet-2-cat')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Usage (HuggingFace Transformers)
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Without [sentence-transformers](https://www.SBERT.net), 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.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('nblokker/debatenet-2-cat')
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model = AutoModel.from_pretrained('nblokker/debatenet-2-cat')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 38 with parameters:
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```
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{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.BatchHardSoftMarginTripletLoss.BatchHardSoftMarginTripletLoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 15,
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"evaluation_steps": 120.5,
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"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 120.5,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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(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})
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)
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```
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## Citing & Authors
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```
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@preprint{blokker2023,
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author = {Blokker, Nico and Blessing, Andre and Dayanik, Erenay and Kuhn, Jonas and Padó, Sebastian and Lapesa, Gabriella},
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note = {To appear in \textit{Language Resources and Evaluation}},
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title = {Between welcome culture and border fence: The {E}uropean refugee crisis in {G}erman newspaper reports},
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url = {https://arxiv.org/abs/2111.10142},
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year = 2023
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}
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@inproceedings{lapesa2020,
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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.},
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address = {Online},
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author = {Lapesa, Gabriella and Blessing, Andre and Blokker, Nico and Dayanik, Erenay and Haunss, Sebastian and Kuhn, Jonas and Padó, Sebastian},
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booktitle = {Proceedings of LREC},
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pages = {919--927},
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title = {{DEbateNet-mig15}: {T}racing the 2015 Immigration Debate in {G}ermany Over Time},
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url = {https://www.aclweb.org/anthology/2020.lrec-1.115},
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year = 2020
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}
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```
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## Acknowledgments
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This model is based on the `sentence-transformers/paraphrase-multilingual-mpnet-base-v2` model:
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```
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "http://arxiv.org/abs/1908.10084",
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}
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```
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- Original model URL: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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- License: Apache 2.0
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## License
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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.
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