Alignment of tokens - 'extract_features_aligned_to_words' from fairseq roberta?
## ❓ Questions & Help

I'm using RoBERTa pretrained model to get embeddings for a dataset. But I want to get the embeddings as per the tokenization which is already present in my dataset. So basically I would want to average the embeddings of a token's BPE if that token in my dataset is getting split into different BPEs. fairseq roberta has a method for this as follows: 

```
import torch
from fairseq.models.roberta import alignment_utils
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
roberta.eval()

example_string_tokens = ['Dr', 'Greenwalt', 'fixed', 'my', 'neck', 'from', 'a', 'snowboard', 'injury', 'and', 'was', 'way', 'more', 'effective', 'that', 'a', 'regular', 'doctor', '.']
doc = roberta.extract_features_aligned_to_words(" ".join(example_string_tokens))

for tok in doc:
    print('{:10}{} (...)'.format(str(tok), tok.vector[:5]))
```  

The output is:

```
<s>       tensor([-0.0656,  0.0189, -0.0003, -0.0907,  0.0550], grad_fn=<SliceBackward>) (...)
Dr        tensor([ 0.2180, -0.0530, -0.3689, -0.0619, -0.6243], grad_fn=<SliceBackward>) (...)
Greenwalt tensor([ 0.3744,  0.0741, -0.7149,  0.0654, -0.1234], grad_fn=<SliceBackward>) (...)
fixed     tensor([ 0.2132,  0.0841, -0.2535, -0.1404, -0.0060], grad_fn=<SliceBackward>) (...)
my        tensor([ 0.1313, -0.0466, -0.1373,  0.1730,  0.1771], grad_fn=<SliceBackward>) (...)
neck      tensor([ 0.0674, -0.3413, -0.0192,  0.0290, -0.3497], grad_fn=<SliceBackward>) (...)
from      tensor([-0.0301, -0.3562, -0.3798,  0.0687,  0.0290], grad_fn=<SliceBackward>) (...)
a         tensor([-0.2496, -0.1036,  0.0270, -0.0819, -0.2146], grad_fn=<SliceBackward>) (...)
snowboard tensor([ 0.4018,  0.1432, -0.0499,  0.2095, -0.0520], grad_fn=<SliceBackward>) (...)
injury    tensor([ 0.0010, -0.6273, -0.0312, -0.1957, -0.4832], grad_fn=<SliceBackward>) (...)
and       tensor([ 0.0747, -0.3335, -0.0593, -0.3805,  0.0930], grad_fn=<SliceBackward>) (...)
was       tensor([ 0.1501, -0.1334, -0.4789, -0.1974, -0.3096], grad_fn=<SliceBackward>) (...)
way       tensor([-0.2803,  0.3204, -0.1663, -0.4420, -0.2641], grad_fn=<SliceBackward>) (...)
more      tensor([-0.1037,  0.1878, -0.5839, -0.4437, -0.1683], grad_fn=<SliceBackward>) (...)
effective tensor([-0.1794,  0.2419, -0.3182, -0.2252, -0.1534], grad_fn=<SliceBackward>) (...)
that      tensor([-0.1146, -0.1935, -0.3615, -0.4998, -0.1000], grad_fn=<SliceBackward>) (...)
a         tensor([-0.2107, -0.2103, -0.1996,  0.0046, -0.1112], grad_fn=<SliceBackward>) (...)
regular   tensor([ 0.2236, -0.0613, -0.5496, -0.3562,  0.1022], grad_fn=<SliceBackward>) (...)
doctor    tensor([ 0.1275, -0.0589, -0.0283, -0.1557, -0.9282], grad_fn=<SliceBackward>) (...)
.         tensor([ 0.1765,  0.0812, -0.1684, -0.2818,  0.0134], grad_fn=<SliceBackward>) (...)
</s>      tensor([-0.0409, -0.0024,  0.0107, -0.0183, -0.0479], grad_fn=<SliceBackward>) (...)


```

I want to be able to do something similar with the hugging transformers library, but I can't find any alignment methods. 

Any suggestions?
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<|||||>It seems this is still unsupported by Huggingface?