Newer versions of Spacy transformer model backends failing

Nobody has claimed this yet.

I use spacy's transformer model for other purposes (such as NER), so re-using the same model made sense.
Looks like Spacy made some tweaks to their syntax which are breaking KeyBERT's spacy backend.

Sample code:
```
from keybert import KeyBERT
from spacy import load

nlp = load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])
kw_model = KeyBERT(model=nlp)

text = "This is a test sentence."

keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 1), stop_words='english', top_n=1, use_mmr=True)
print(keywords)

```
Expected behavior:
prints [("test", ...)]

Observed behavior:

```
Traceback (most recent call last):
File "...\anaconda3\envs\env\lib\site-packages\keybert\backend\_spacy.py", line 84, in embed
self.embedding_model(doc)._.trf_data.tensors[-1][0].tolist()
AttributeError: 'DocTransformerOutput' object has no attribute 'tensors'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "...\test.py", line 9, in
keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 1), stop_words='english', top_n=1, use_mmr=True)
File "...\envs\env\lib\site-packages\keybert\_model.py", line 195, in extract_keywords
doc_embeddings = self.model.embed(docs)
File "...\envs\env\lib\site-packages\keybert\backend\_spacy.py", line 88, in embed
self.embedding_model("An empty document")
AttributeError: 'DocTransformerOutput' object has no attribute 'tensors'
```

Package versions:
cupy-cuda11x 12.3.0
curated-tokenizers 0.0.9
curated-transformers 0.1.1
en-core-web-trf 3.7.3
keybert 0.8.5
keyphrase-vectorizers 0.0.13
safetensors 0.4.4
scikit-learn 1.5.1
scipy 1.13.1
sentence-transformers 3.0.1
spacy 3.7.5
spacy-alignments 0.9.1
spacy-curated-transformers 0.2.2
spacy-legacy 3.0.12
spacy-loggers 1.0.5
spacy-transformers 1.3.5
thinc 8.2.5
tokenizers 0.15.2
transformers 4.36.2

Start with keybert/backend/_spacy.py and the KeyBERT.extract_keywords path, then reproduce the sample with the listed spaCy 3.7.5 and transformer packages. Verify that the spaCy backend handles the current transformer output and that the example returns the expected keyword without the reported AttributeError.

Written by the indexing model from the issue text.