TSjB/labse-qm
It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Fine-tined by Bogdan Tewunalany
Based on LaBSE
Usage (Sentence-Transformers)
Python:
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", "Бу айтым юлгюдю"]
model = SentenceTransformer('TSjB/labse-qm')
embeddings = model.encode(sentences)
print(embeddings)
R language:
library(data.table)
library(reticulate)
library(ggplot2)
library(ggrepel)
library(Rtsne)
py_install("sentence-transformers", pip = TRUE)
st <- import("sentence_transformers")
english_sentences = base::c("dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog.")
italian_sentences = base::c("cane", "I cuccioli sono carini.", "Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.")
qarachay_sentences = base::c("ит", "Итле джагъымлыдыла.", "Джагъа юсю бла итим бла айланыргъа сюеме.")
model = st$SentenceTransformer('TSjB/labse-qm')
english_embeddings = model$encode(english_sentences)
italian_embeddings = model$encode(italian_sentences)
qarachay_embeddings = model$encode(qarachay_sentences)
m <- rbind(english_embeddings,
italian_embeddings,
qarachay_embeddings) %>% as.matrix
tsne <- Rtsne(m, perplexity = floor((nrow(m) - 1) / 3))
tSNE_df <- tsne$Y %>%
as.data.table() %>%
setnames(old = c("V1", "V2"), new = c("tSNE1", "tSNE2")) %>%
.[, `:=`(sentence = c(english_sentences, italian_sentences, qarachay_sentences),
language = c(rep("english", length(english_sentences)),
rep("italian", length(italian_sentences)),
rep("qarachay", length(qarachay_sentences))))]
tSNE_df %>%
ggplot(aes(x = tSNE1,
y = tSNE2,
color = language,
label = sentence
)
) +
geom_label_repel() +
geom_point()
Evaluation Results
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 6439 with parameters:
{'batch_size': 8, '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": 1,
"evaluation_steps": 100,
"evaluator": "__main__.ChainScoreEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
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