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pipeline_tag: sentence-similarity
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - es
  - hackathon-pln-es/nli-es
  - text: >-
      A ver si nos tenemos que poner todos en huelga hasta cobrar lo que
  - text: >-
      La huelga es el método de lucha más eficaz para conseguir mejoras en el
  - text: Tendremos que optar por hacer una huelga para cobrar lo que queremos.
  - text: Queda descartada la huelga aunque no cobremos lo que queramos.


This is a sentence-transformers model trained on a collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Based around the siamese networks approach from this paper.

You can see a demo for this model here.

You can find our other model, paraphrase-spanish-distilroberta here and its demo here.

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
sentences = ["Este es un ejemplo", "Cada oración es transformada"]

model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
embeddings = model.encode(sentences)

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('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
model = AutoModel.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')

# 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:")

Evaluation Results

Our model was evaluated on the task of Semantic Textual Similarity using the SemEval-2015 Task for Spanish. We measure

BETO STS BERTIN STS (this model) Relative improvement
cosine_pearson 0.609803 0.683188 +12.03
cosine_spearman 0.528776 0.615916 +16.48
euclidean_pearson 0.590613 0.672601 +13.88
euclidean_spearman 0.526529 0.611539 +16.15
manhattan_pearson 0.589108 0.672040 +14.08
manhattan_spearman 0.525910 0.610517 +16.09
dot_pearson 0.544078 0.600517 +10.37
dot_spearman 0.460427 0.521260 +13.21


The model was trained with the parameters:


We used a collection of datasets of Natural Language Inference as training data:

  • ESXNLI, only the part in spanish
  • SNLI, automatically translated
  • MultiNLI, automatically translated

The whole dataset used is available here.

Here we leave the trick we used to increase the amount of data for training here:

  for row in reader:
    if row['language'] == 'es':
      sent1 = row['sentence1'].strip()
      sent2 = row['sentence2'].strip()
      add_to_samples(sent1, sent2, row['gold_label'])
      add_to_samples(sent2, sent1, row['gold_label'])  #Also add the opposite


sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 1818 with parameters:

{'batch_size': 64}


sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:

{'scale': 20.0, 'similarity_fct': 'cos_sim'}

Parameters of the fit()-Method:

    "epochs": 10,
    "evaluation_steps": 0,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 909,
    "weight_decay": 0.01

Full Model Architecture

  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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})


Anibal Pérez,

Emilio Tomás Ariza,

Lautaro Gesuelli y

Mauricio Mazuecos.