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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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# keyphrase-mpnet-v1 |
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This is a [sentence-transformers](https://www.SBERT.net) model specialized for phrases: It maps phrases to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. In the original paper, this model is used for calculating semantic-based evaluation metrics of keyphrase models. |
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This model is based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and further fine-tuned on 1 million keyphrase data with SimCSE. |
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## Citing & Authors |
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Paper: [KPEval: Towards Fine-grained Semantic-based Evaluation of Keyphrase Extraction and Generation Systems](https://arxiv.org/abs/2303.15422) |
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``` |
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@article{wu2023kpeval, |
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title={KPEval: Towards Fine-grained Semantic-based Evaluation of Keyphrase Extraction and Generation Systems}, |
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author={Di Wu and Da Yin and Kai-Wei Chang}, |
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year={2023}, |
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eprint={2303.15422}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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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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phrases = ["information retrieval", "text mining", "natural language processing"] |
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model = SentenceTransformer('uclanlp/keyphrase-mpnet-v1') |
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embeddings = model.encode(phrases) |
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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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phrases = ["information retrieval", "text mining", "natural language processing"] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('uclanlp/keyphrase-mpnet-v1') |
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model = AutoModel.from_pretrained('uclanlp/keyphrase-mpnet-v1') |
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# Tokenize sentences |
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encoded_input = tokenizer(phrases, 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("Phrase embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Training |
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The model is trained on phrases from four keyphrase datasets covering a wide range of domains. |
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| Dataset Name | Domain | Number of Phrases | |
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|-------------------------------------------------------------|---------------|-------------------| |
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| [KP20k](https://www.aclweb.org/anthology/P17-1054/) | Science | 715369 | |
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| [KPTimes](https://www.aclweb.org/anthology/W19-8617/) | News | 113456 | |
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| [StackEx](https://www.aclweb.org/anthology/2020.acl-main.710/) | Online Forum | 8149 | |
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| [OpenKP](https://www.aclweb.org/anthology/D19-1521/) | Web | 200335 | |
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| **Total** | | **1030309** | |
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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 2025 with parameters: |
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``` |
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{'batch_size': 512, '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.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
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``` |
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{'scale': 20.0, 'similarity_fct': 'cos_sim'} |
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``` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 1, |
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"evaluation_steps": 0, |
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"evaluator": "NoneType", |
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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": 1e-06 |
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}, |
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"scheduler": "WarmupLinear", |
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"steps_per_epoch": null, |
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"warmup_steps": 203, |
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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': 12, 'do_lower_case': False}) with Transformer model: MPNetModel |
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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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