metadata

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

ST-NLI-ca_paraphrase-multilingual-mpnet-base

This is a sentence-transformers 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 using NLI data. Specifically, it was trained on two Catalan NLI datasets: TE-ca 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 installed:

pip install -U sentence-transformers

Then you can use the model like this:

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:

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, 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.

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), and on two paraphrase identification tasks in Catalan: 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 [email protected]