sbert-ruquad
sbert-ruquald 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.
The model is based on the distiluse-base-multilingual-cased-v2, fine-tuned on RUQuAD - a question-answer dataset for Icelandic.
The data used for this model contains approximately question-span and question-paragraph pairs, with 14920 pairs used for training under the MultipleNegativesRankingLoss.
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 = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('language-and-voice-lab/sbert-ruquad')
embeddings = model.encode(sentences)
print(embeddings)
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('language-and-voice-lab/sbert-ruquad')
model = AutoModel.from_pretrained('language-and-voice-lab/sbert-ruquad')
# 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
The model was evaluated with a hold-out set from the original data using the BinaryClassificationEvaluator approach.
cossim_accuracy | cossim_f1 | cossim_precision | cossim_recall | cossim_ap | manhattan_accuracy | manhattan_f1 | manhattan_precision | manhattan_recall | manhattan_ap | euclidean_accuracy | euclidean_f1 | euclidean_precision | euclidean_recall | euclidean_ap | dot_accuracy | dot_f1 | dot_precision | dot_recall | dot_ap |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.913616792 | 0.910709318 | 0.942429476 | 0.881054898 | 0.968807199 | 0.869483315 | 0.856401384 | 0.922360248 | 0.799246502 | 0.932638132 | 0.869214209 | 0.857062937 | 0.892253931 | 0.824542519 | 0.932737722 | 0.914962325 | 0.911732456 | 0.929050279 | 0.895048439 | 0.968732732 |
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Training
The model was trained with the parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader
of length 933 with parameters:
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss
with parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
Parameters of the fit()-Method:
{
"epochs": 20,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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
Stefán Ólafsson (stefanola@ru.is) trained the model. Njáll Skarphéðinsson et al. created the RUQuAD dataset.
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