3.2 KiB
3.2 KiB
pipeline_tag, tags, widget
| pipeline_tag | tags | widget | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sentence-similarity |
|
|
Sentence Embeddings with distilbert-zwnj-wnli-mean-tokens
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 = [
'اولین حکمران شهر بابل کی بود؟',
'در فصل زمستان چه اتفاقی افتاد؟',
'میراث کوروش'
]
model = SentenceTransformer('m3hrdadfi/distilbert-zwnj-wnli-mean-tokens')
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
# Max Pooling - Take the max value over time for every dimension.
def max_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()
token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value
return torch.mean(token_embeddings, 1)[0]
# Sentences we want sentence embeddings for
sentences = [
'اولین حکمران شهر بابل کی بود؟',
'در فصل زمستان چه اتفاقی افتاد؟',
'میراث کوروش'
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/distilbert-zwnj-wnli-mean-tokens')
model = AutoModel.from_pretrained('m3hrdadfi/distilbert-zwnj-wnli-mean-tokens')
# 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, max pooling.
sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Questions?
Post a Github issue from HERE.