metadata

tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - SbertDistil
license: apache-2.0
datasets:
  - wikimedia/wikipedia
  - SiberiaSoft/SiberianPersonaChat-2
language:
  - ru
  - en
metrics:
  - mse
library_name: transformers

FractalGPT/SbertDistil

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This is a fast and small model for solving the problem of determining the proximity between sentences, in the future we will reduce and speed it up. Project

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

Run example in Collab

pip install -U sentence-transformers

Then you can use the model like this:

import numpy as np
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('FractalGPT/SbertDistil')

def cos(x, y):
  return np.dot(x, y)/(np.linalg.norm(x)*np.linalg.norm(y))

text_1 = "Кто такой большой кот?"
text_2 = "Who is kitty?"
a = model.encode(text_1)
b = model.encode(text_2)
cos(a, b)

>>> 0.8072159157330788

Training

The original weights was taken from cointegrated/rubert-tiny2.
Training was conducted in two stages:

In the first stage, the model was trained on Wikipedia texts (4 million texts) for three epochs.
In the second stage, training was conducted on Wikipedia and dialog dataset for one epoch.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 312, 'out_features': 384, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)