Getting Word Embeddings for Sentences using long-former model?
I am new to **Huggingface** and have few basic queries. This post might be helpful to others as well who are starting to use **longformer** model from  **huggingface**. 

## Objective:
Create Sentence/document embeddings using **longformer** model.  We don't have lables in our data-set, so we want to do clustering on output of embeddings generated. Please let me know if the code is correct?

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` **version:3.0.2**
- Platform:
- Python version: **Python 3.6.12 :: Anaconda, Inc.**
- PyTorch version (GPU?):**1.7.1**
- Tensorflow version (GPU?): **2.3.0**
- Using GPU in script?: **Yes**
- Using distributed or parallel set-up in script?: **parallel**

### Who can help
@patrickvonplaten

##Models:

- longformer, reformer, transfoxl, xlnet: @patrickvonplaten


Library:

- benchmarks: @patrickvonplaten
- text generation: @patrickvonplaten
- tokenizers: @LysandreJik
- trainer: @sgugger



## Information

Model I am using longformer:

The problem arises when using:
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] my own task or dataset: (give details below)


## Code:
```
from transformers import LongformerModel, LongformerTokenizer
model = LongformerModel.from_pretrained('allenai/longformer-base-4096',output_hidden_states = True)
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')

# Put the model in "evaluation" mode, meaning feed-forward operation.
model.eval()

df = pd.read_csv("inshort_news_data-1.csv")
df.head(5)
#**news_article** column is used to generate embedding.
```
```
all_content=list(df['news_article'])
def sentence_bert():
    list_of_emb=[]
    for i in range(len(all_content)):
        SAMPLE_TEXT = all_content[i]  # long input document
        print("length of string:  ",len(SAMPLE_TEXT.split()))
        input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  

        # How to include batch of size here?

        # Attention mask values -- 0: no attention, 1: local attention, 2: global attention
        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
        attention_mask[:, [0,-1]] = 2
        
         with torch.no_grad():

            outputs = model(input_ids, attention_mask=attention_mask)
            hidden_states = outputs[2]
            token_embeddings = torch.stack(hidden_states, dim=0)
            # Remove dimension 1, the "batches".
            token_embeddings = torch.squeeze(token_embeddings, dim=1)
            # Swap dimensions 0 and 1.
            token_embeddings = token_embeddings.permute(1,0,2)

            token_vecs_sum = []
            # For each token in the sentence...
            for token in token_embeddings:

            #but preferrable is
               sum_vec=torch.sum(token[-4:],dim=0)

            # Use `sum_vec` to represent `token`.
               token_vecs_sum.append(sum_vec)

           
           h=0
           for i in  range(len(token_vecs_sum)):
              h+=token_vecs_sum[i]
           list_of_emb.append(h)

    return list_of_emb

f=sentence_bert()

```

## Doubts/Question:

1. If we want to get embeddings in batches, what all changes do I need to make in the above code?
2. If the sentence is " **I am learning longformer model**.". Will the tokenizer function will return ID's of following token in longformer model: **[ 'I', 'am' , 'learning' , 'longformer' , 'model. ']** Is my understanding correct? Can you explain it with minimum reproducible example?
3. Similarly attention mask will return attention weights of following tokens? The part which I didn't understand is its necessary to replace last attention weight of sentence by **2** (in above code)?
4. #outputs[0] gives us sequence_output:  torch.Size([768])
       #outputs[1] gives us pooled_output torch.Size([1, 512, 768])
       #outputs[2]: gives us Hidden_output:  torch.Size([13, 512, 768])
        Can you talk more about what does each dimension depicts in outputs? Example what does Hidden_output [13, 512, 768] 
         means ? From where **13, 512 and 768 is coming** ? What does 13, 512 and 768 means in terms of hidden state, embedding dimesion and number of layesr?
5.  From which token do we get the sentence embedding in **longformer**? Can you explain it with minimum reproducible example?
6. If I am running the model in linux system, where does pre-trained model get's downloaded or stored? Can you list the complete path?
7. length of string:   **15**
**input_ids:   tensor([[    0, 35702,  1437,  3743,  1437,   560,  1437, 48317,  1437, 28884,
         20042,  1437,  6968,   241,  1437, 16402,  1437,   463,  1437,  3056,
          1437, 48317,  1437,   281,  1437, 16752,  1437,   281,  1437,  1694,
          1437,  7424,     4,     2]])**
**input_ids.shape:   torch.Size([1, 34])**
My sentence length is **15** then why input_ids and attention_ids are length **34**?



## Expected behavior

Document1:  Embeddings
Document2: Embeddings


Please use the [forums](https://discuss.huggingface.co/) for this kind of questions, we keep the issues for bugs and feature requests only.<|||||>> Please use the [forums](https://discuss.huggingface.co/) for this kind of questions, we keep the issues for bugs and feature requests only.

Thanks for pointing it out. I have posted my question in https://discuss.huggingface.co/
Sorry for confusion.<|||||>It is saying my account is on hold. <|||||>What's your username? You have to read a few posts (that's part of the antispam strategy of discourse) but I can bump your username to avoid that.<|||||>> What's your username? You have to read a few posts (that's part of the antispam strategy of discourse) but I can bump your username to avoid that.

My username is **pchhapolika**<|||||>Ah saw your post and approved it.<|||||>> Ah saw your post and approved it.

Thanks a lot. Closing this request. 

Note this question has been moved here: https://discuss.huggingface.co/t/getting-word-embeddings-for-sentences-using-long-former-model/8448