FractalGPT/SbertDistil

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

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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. <img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/DistilSBERT/Train/1sten.JPG?raw=true" width=700 />
• —In the second stage, training was conducted on Wikipedia and dialog dataset for one epoch. <img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/DistilSBERT/Train/2sten.JPG?raw=true" width=700 />

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'})
)