PatentSBERTa / README.md

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	---
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	---

	# PatentSBERTa


	## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

	### Aalborg University Business School, AI: Growth-Lab

	https://arxiv.org/abs/2103.11933

	https://github.com/AI-Growth-Lab/PatentSBERTa


	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.

	<!--- Describe your model here -->

	## Usage (Sentence-Transformers)

	Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

	```
	pip install -U sentence-transformers
	```

	Then you can use the model like this:

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

	model = SentenceTransformer('AI-Growth-Lab/PatentSBERTa')
	embeddings = model.encode(sentences)
	print(embeddings)
	```



	## Usage (HuggingFace Transformers)
	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.

	```python
	from transformers import AutoTokenizer, AutoModel
	import torch


	def cls_pooling(model_output, attention_mask):
	return model_output[0][:,0]


	# 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('AI-Growth-Lab/PatentSBERTa')
	model = AutoModel.from_pretrained('AI-Growth-Lab/PatentSBERTa')

	# 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, cls pooling.
	sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

	print("Sentence embeddings:")
	print(sentence_embeddings)
	```



	## Evaluation Results

	<!--- Describe how your model was evaluated -->

	For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


	## Training
	The model was trained with the parameters:

	DataLoader:

	`torch.utils.data.dataloader.DataLoader` of length 5 with parameters:
	```
	{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
	```

	Loss:

	`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`

	Parameters of the fit()-Method:
	```
	{
	"epochs": 1,
	"evaluation_steps": 0,
	"evaluator": "NoneType",
	"max_grad_norm": 1,
	"optimizer_class": "<class 'transformers.optimization.AdamW'>",
	"optimizer_params": {
	"lr": 2e-05
	},
	"scheduler": "WarmupLinear",
	"steps_per_epoch": null,
	"warmup_steps": 100,
	"weight_decay": 0.01
	}
	```


	## Full Model Architecture
	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
	)
	```

	## Citing & Authors

	```LaTeX
	@article{bekamiri2021patentsberta,
	title={PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT},
	author={Bekamiri, Hamid and Hain, Daniel S and Jurowetzki, Roman},
	journal={arXiv preprint arXiv:2103.11933},
	year={2021}
	}
	```