86 lines
3.2 KiB
Markdown
86 lines
3.2 KiB
Markdown
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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widget:
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source_sentence: "مردی در حال خوردن پاستا است."
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sentences:
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- 'مردی در حال خوردن خوراک است.'
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- 'مردی در حال خوردن یک تکه نان است.'
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- 'دختری بچه ای را حمل می کند.'
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- 'یک مرد سوار بر اسب است.'
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- 'زنی در حال نواختن پیانو است.'
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- 'دو مرد گاری ها را به داخل جنگل هل دادند.'
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- 'مردی در حال سواری بر اسب سفید در مزرعه است.'
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- 'میمونی در حال نواختن طبل است.'
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- 'یوزپلنگ به دنبال شکار خود در حال دویدن است.'
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---
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# Sentence Embeddings with `distilbert-zwnj-wnli-mean-tokens`
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = [
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'اولین حکمران شهر بابل کی بود؟',
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'در فصل زمستان چه اتفاقی افتاد؟',
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'میراث کوروش'
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]
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model = SentenceTransformer('m3hrdadfi/distilbert-zwnj-wnli-mean-tokens')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Usage (HuggingFace Transformers)
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Without [sentence-transformers](https://www.SBERT.net), 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.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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# Max Pooling - Take the max value over time for every dimension.
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def max_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value
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return torch.mean(token_embeddings, 1)[0]
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# Sentences we want sentence embeddings for
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sentences = [
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'اولین حکمران شهر بابل کی بود؟',
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'در فصل زمستان چه اتفاقی افتاد؟',
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'میراث کوروش'
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]
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/distilbert-zwnj-wnli-mean-tokens')
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model = AutoModel.from_pretrained('m3hrdadfi/distilbert-zwnj-wnli-mean-tokens')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, max pooling.
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sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Questions?
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Post a Github issue from [HERE](https://github.com/m3hrdadfi/sentence-transformers).
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