Train a spaCy model for semantic similarity

I'm attempting to train a spaCy model for the purposes of computing semantic similarity but I'm not getting the results I would anticipate.

I have created two text files that contain many sentences that use a new term, PROJ123456. For example, PROJ123456 is on track.

I've added each to a DocBin and saved them to disk as train.spacy and dev.spacy.

I'm then running: python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./dev.spacy

The config.cfg file contains:

[paths]
train = null
dev = null
vectors = null
init_tok2vec = null

[system]
gpu_allocator = null
seed = 0

[nlp]
lang = en
pipeline = [tok2vec,parser]
batch_size = 1000
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {@tokenizers:spacy.Tokenizer.v1}

[components]

[components.parser]
factory = parser
learn_tokens = false
min_action_freq = 30
moves = null
scorer = {@scorers:spacy.parser_scorer.v1}
update_with_oracle_cut_size = 100

[components.parser.model]
@architectures = spacy.TransitionBasedParser.v2
state_type = parser
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null

[components.parser.model.tok2vec]
@architectures = spacy.Tok2VecListener.v1
width = ${components.tok2vec.model.encode.width}
upstream = *

[components.tok2vec]
factory = tok2vec

[components.tok2vec.model]
@architectures = spacy.Tok2Vec.v2

[components.tok2vec.model.embed]
@architectures = spacy.MultiHashEmbed.v2
width = ${components.tok2vec.model.encode.width}
attrs = [ORTH,SHAPE]
rows = [5000,2500]
include_static_vectors = true

[components.tok2vec.model.encode]
@architectures = spacy.MaxoutWindowEncoder.v2
width = 256
depth = 8
window_size = 1
maxout_pieces = 3

[corpora]

[corpora.dev]
@readers = spacy.Corpus.v1
path = ${paths.dev}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null

[corpora.train]
@readers = spacy.Corpus.v1
path = ${paths.train}
max_length = 0
gold_preproc = false
limit = 0
augmenter = null

[training]
dev_corpus = corpora.dev
train_corpus = corpora.train
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null

[training.batcher]
@batchers = spacy.batch_by_words.v1
discard_oversize = false
tolerance = 0.2
get_length = null

[training.batcher.size]
@schedules = compounding.v1
start = 100
stop = 1000
compound = 1.001
t = 0.0

[training.logger]
@loggers = spacy.ConsoleLogger.v1
progress_bar = false

[training.optimizer]
@optimizers = Adam.v1
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 0.00000001
learn_rate = 0.001

[training.score_weights]
dep_uas = 0.5
dep_las = 0.5
dep_las_per_type = null
sents_p = null
sents_r = null
sents_f = 0.0

[pretraining]

[initialize]
vectors = en_core_web_lg
init_tok2vec = ${paths.init_tok2vec}
vocab_data = null
lookups = null
before_init = null
after_init = null

[initialize.components]

[initialize.tokenizer]

I get a new model in output/model-last.

I then run the following file:

import spacy
nlp = spacy.load(./output/model-last)
print(nlp('PROJ123456').vector)

I'm expecting to see a vector with some non-zero values but instead I see a vector of 300 zero values. I take that to indicate it hasn't added PROJ123456 to the vocab. But I'm not sure why.