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

	---

	# ST-NLI-ca_paraphrase-multilingual-mpnet-base

	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.

	It has been developed through further training of a multilingual fine-tuned model, [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) using NLI data. Specifically, it was trained on two Catalan NLI datasets: [TE-ca](https://huggingface.co/datasets/projecte-aina/teca) and the professional translation of XNLI into Catalan. The training employed the Multiple Negatives Ranking Loss with Hard Negatives, which leverages triplets composed of a premise, an entailed hypothesis, and a contradiction. It is important to note that, given this format, neutral hypotheses from the NLI datasets were not used for training. Additionally, as a form of data augmentation, the model's training set was expanded by duplicating the triplets, wherein the order of the premise and entailed hypothesis was reversed, resulting in a total of 18,928 triplets.

	## 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, util
	sentences = ["This is an example sentence", "Each sentence is converted"]

	model = SentenceTransformer('{MODEL_NAME}')
	embeddings = model.encode(sentences)
	print(embeddings)
	```

	For instance, to sort a list of sentences by their similarity to a reference sentence, the following code can be used:

	```python
	reference_sent = "Avui és un bon dia."
	sentences = [
	"M'agrada el dia que fa.",
	"Tothom té un mal dia.",
	"És dijous.",
	"Fa un dia realment dolent",
	]

	reference_sent_embedding = model.encode(reference_sent)
	similarity_scores = {}
	for sentence in sentences:
	sent_embedding = model.encode(sentence)
	cosine_similarity = util.pytorch_cos_sim(reference_sent_embedding, sent_embedding)
	similarity_scores[float(cosine_similarity.data[0][0])] = sentence

	print(f"Sentences in order of similarity to '{reference_sent}' (from max to min):")
	for i, (cosine_similarity,sent) in enumerate(dict(sorted(similarity_scores.items(), reverse=True)).items()):
	print(f"{i}) '{sent}': {cosine_similarity}")
	```


	## 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


	#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('{MODEL_NAME}')
	model = AutoModel.from_pretrained('{MODEL_NAME}')

	# 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)
	```



	## Evaluation Results

	We evaluated the model on the test set of the Catalan Semantic Text Similarity ([STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca)), and on two paraphrase identification tasks in Catalan: [Parafraseja](https://huggingface.co/datasets/projecte-aina/Parafraseja) and the professional translation of PAWS into Catalan.

	\| STS-ca (Pearson) \| Parafraseja (acc) \| PAWS-ca (acc) \|
	\|------------------\|-------------------\|---------------\|
	\| 0.65 \| 0.72 \| 0.65 \|


	## Training
	The model was trained with the parameters:

	DataLoader:

	`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 147 with parameters:
	```
	{'batch_size': 128}
	```

	Loss:

	`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
	```
	{'scale': 20.0, 'similarity_fct': 'cos_sim'}
	```

	Parameters of the fit()-Method:
	```
	{
	"epochs": 1,
	"evaluation_steps": 14,
	"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
	"max_grad_norm": 1,
	"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
	"optimizer_params": {
	"lr": 2e-05
	},
	"scheduler": "WarmupLinear",
	"steps_per_epoch": null,
	"warmup_steps": 15,
	"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})
	)
	```

	## Citing & Authors

	For further information, send an email to aina@bsc.es