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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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--- |
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# Model |
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This [sentence-transformers](https://www.SBERT.net) model model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset. |
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Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/) |
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Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth |
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# Claim Quality Classification |
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We cast this task as a pairwise classification task, where the objective is to compare two versions of the same claim and determine which one is better. We train this model by fine-tuning SBERT based on bert-base-cased using a siamese network structure with softmax loss. Outputs can also be used to rank multiple versions of the same claim, for example, using [SVMRank](https://github.com/ds4dm/PySVMRank) or BTL (Bradley-Terry-Luce model). |
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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('gabski/sbert-relative-claim-quality') |
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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('gabski/sbert-relative-claim-quality') |
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model = AutoModel.from_pretrained('gabski/sbert-relative-claim-quality') |
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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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## 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: BertModel |
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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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```bibtex |
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@inproceedings{skitalinskaya-etal-2021-learning, |
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title = "Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale", |
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author = "Skitalinskaya, Gabriella and |
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Klaff, Jonas and |
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Wachsmuth, Henning", |
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booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", |
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month = apr, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.eacl-main.147", |
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doi = "10.18653/v1/2021.eacl-main.147", |
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pages = "1718--1729", |
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} |
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``` |