imvladikon/sentence-transformers-alephbert[WIP]
This 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.
Current version is distillation of the LaBSE model on private corpus.
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
from sentence_transformers.util import cos_sim
sentences = [
"הם היו שמחים לראות את האירוע שהתקיים.",
"לראות את האירוע שהתקיים היה מאוד משמח להם."
]
model = SentenceTransformer('imvladikon/sentence-transformers-alephbert')
embeddings = model.encode(sentences)
print(cos_sim(*tuple(embeddings)).item())
# 0.883316159248352
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.
import torch
from torch import nn
from transformers import AutoTokenizer, AutoModel
#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 = [
"הם היו שמחים לראות את האירוע שהתקיים.",
"לראות את האירוע שהתקיים היה מאוד משמח להם."
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('imvladikon/sentence-transformers-alephbert')
model = AutoModel.from_pretrained('imvladikon/sentence-transformers-alephbert')
# 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'])
cos_sim = nn.CosineSimilarity(dim=0, eps=1e-6)
print(cos_sim(sentence_embeddings[0], sentence_embeddings[1]).item())
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 44999 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": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 44999,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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
@misc{seker2021alephberta,
title={AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With},
author={Amit Seker and Elron Bandel and Dan Bareket and Idan Brusilovsky and Refael Shaked Greenfeld and Reut Tsarfaty},
year={2021},
eprint={2104.04052},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{reimers2019sentencebert,
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author={Nils Reimers and Iryna Gurevych},
year={2019},
eprint={1908.10084},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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