
# Sentence Embeddings with `distilbert-zwnj-wnli-mean-tokens`

## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
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](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.

```python
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](https://github.com/m3hrdadfi/sentence-transformers).
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "\u0645\u0631\u062f\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062e\u0648\u0631\u062f\u0646 \u067e\u0627\u0633\u062a\u0627 \u0627\u0633\u062a.", "sentences": ["\u0645\u0631\u062f\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062e\u0648\u0631\u062f\u0646 \u062e\u0648\u0631\u0627\u06a9 \u0627\u0633\u062a.", "\u0645\u0631\u062f\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062e\u0648\u0631\u062f\u0646 \u06cc\u06a9 \u062a\u06a9\u0647 \u0646\u0627\u0646 \u0627\u0633\u062a.", "\u062f\u062e\u062a\u0631\u06cc \u0628\u0686\u0647 \u0627\u06cc \u0631\u0627 \u062d\u0645\u0644 \u0645\u06cc \u06a9\u0646\u062f.", "\u06cc\u06a9 \u0645\u0631\u062f \u0633\u0648\u0627\u0631 \u0628\u0631 \u0627\u0633\u0628 \u0627\u0633\u062a.", "\u0632\u0646\u06cc \u062f\u0631 \u062d\u0627\u0644 \u0646\u0648\u0627\u062e\u062a\u0646 \u067e\u06cc\u0627\u0646\u0648 \u0627\u0633\u062a.", "\u062f\u0648 \u0645\u0631\u062f \u06af\u0627\u0631\u06cc \u0647\u0627 \u0631\u0627 \u0628\u0647 \u062f\u0627\u062e\u0644 \u062c\u0646\u06af\u0644 \u0647\u0644 \u062f\u0627\u062f\u0646\u062f.", "\u0645\u0631\u062f\u06cc \u062f\u0631 \u062d\u0627\u0644 \u0633\u0648\u0627\u0631\u06cc \u0628\u0631 \u0627\u0633\u0628 \u0633\u0641\u06cc\u062f \u062f\u0631 \u0645\u0632\u0631\u0639\u0647 \u0627\u0633\u062a.", "\u0645\u06cc\u0645\u0648\u0646\u06cc \u062f\u0631 \u062d\u0627\u0644 \u0646\u0648\u0627\u062e\u062a\u0646 \u0637\u0628\u0644 \u0627\u0633\u062a.", "\u06cc\u0648\u0632\u067e\u0644\u0646\u06af \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u0634\u06a9\u0627\u0631 \u062e\u0648\u062f \u062f\u0631 \u062d\u0627\u0644 \u062f\u0648\u06cc\u062f\u0646 \u0627\u0633\u062a."]}}
sentence-transformers pytorch distilbert feature-extraction sentence-similarity transformers endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 


# Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens'

## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:



## 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.



## Questions?
Post a Github issue from HERE.
# Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens' ## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## 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. ## Questions?
Post a Github issue from HERE.
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 
 # Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens' ## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## 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. ## Questions?
Post a Github issue from HERE.
passage: TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 
# Sentence Embeddings with 'distilbert-zwnj-wnli-mean-tokens'## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:## 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.## Questions?
Post a Github issue from HERE.