How to generate BERT/Roberta word/sentence embedding?
I know the stanford operation. 
```python
tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
model = RobertaModel.from_pretrained('roberta-large')

input_ids = torch.tensor(tokenizer.encode("Hello, my <span class="highlighter highlight-on">dog</span> is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
outputs = model(input_ids)

last_hidden_states = outputs[0] #(batch_size, input_len, embedding_size) But I need single vector for each sentence 
```

But. I am working on improving RNN with incorporating Bert-like pretrain model embedding. How to get a sentence embedding so in this case(one vector for entire sentence)?   Averaging or some transformation of the last_hidden_states?  Is `add_special_token` necessary? Any suggested papers to read?
Hi there. A few weeks or months ago, I wrote this notebook to introduce my colleagues to doing inference on LMs. In other words: how can I get a sentence representation out of them. You can have a look [here](https://github.com/BramVanroy/bert-for-inference/blob/master/introduction-to-bert.ipynb). It should be self-explanatory.<|||||>Hey @zjplab, for sentence embeddings, I'd recommend this library https://github.com/UKPLab/sentence-transformers along with their paper. They explain how they get their sentence embeddings as well as the pros and cons to several different methods of doing it. They have embeddings for bert/roberta and many more<|||||>There's also spaCy's wrapper of transformers [spacy-transformers](https://github.com/explosion/spacy-transformers). Can compare sentences to each other, and access sentence embeddings:

[examples/Spacy_Transformers_Demo.ipynb](https://github.com/explosion/spacy-transformers/blob/master/examples/Spacy_Transformers_Demo.ipynb)
```python
# $ pip install spacy-transformers
# $ python -m spacy download en_trf_bertbaseuncased_lg

import spacy
nlp = spacy.load("en_trf_bertbaseuncased_lg")
apple1 = nlp("Apple shares rose on the news.")
apple2 = nlp("Apple sold fewer iPhones this quarter.")
apple3 = nlp("Apple pie is delicious.")

# sentence similarity
print(apple1.similarity(apple2)) #0.69861203
print(apple1.similarity(apple3)) #0.5404963

# sentence embeddings
apple1.vector  # or apple1.tensor.sum(axis=0)
```

I'm fairly confident `apple1.vector` is the sentence embedding, but someone will want to double-check.

[Edit] spacy-transformers currenty requires transformers==2.0.0, which is pretty far behind. It also doesn't let you embed batches (one sentence at a time). I'm gonna use UKPLab/sentence-transformers, personally.<|||||>> There's also spaCy's wrapper of transformers [spacy-transformers](https://github.com/explosion/spacy-transformers). Can compare sentences to each other, and access sentence embeddings:
> 
> [examples/Spacy_Transformers_Demo.ipynb](https://github.com/explosion/spacy-transformers/blob/master/examples/Spacy_Transformers_Demo.ipynb)
> 
> ```python
> # $ pip install spacy-transformers
> # $ python -m spacy download en_trf_bertbaseuncased_lg
> 
> import spacy
> nlp = spacy.load("en_trf_bertbaseuncased_lg")
> apple1 = nlp("Apple shares rose on the news.")
> apple2 = nlp("Apple sold fewer iPhones this quarter.")
> apple3 = nlp("Apple pie is delicious.")
> 
> # sentence similarity
> print(apple1.similarity(apple2)) #0.69861203
> print(apple1.similarity(apple3)) #0.5404963
> 
> # sentence embeddings
> apple1.vector  # or apple1.tensor.sum(axis=0)
> ```
> 
> I'm fairly confident `apple1.vector` is the sentence embedding, but someone will want to double-check.
> 
> [Edit] spacy-transformers currenty requires transformers==2.0.0, which is pretty far behind. It also doesn't let you embed batches (one sentence at a time). I'm gonna use UKPLab/sentence-transformers, personally.

Is there any way to compare a contextualized word embedding with a word embedding? Let's say I have a sentence "Apples are delicious" and I want to compare the similarity of the contextualized word "apples" against words such as "fruit" or "company". Is there any way to do so with transformers like BERT that could deliver reliable numbers? Thanks in advance.
<|||||>This one seems to do the job too: [https://github.com/ashokc/Bow-to-Bert](https://github.com/ashokc/Bow-to-Bert), accompanied with this blog post [http://xplordat.com/2019/09/23/bow-to-bert/](http://xplordat.com/2019/09/23/bow-to-bert/)