Can I stop the training once i reached a desirable outcome?

[HarounAbdelsamad]

I am training an en_core_web_trf pre model for the last 15 hours and the process still did not end? Can I end it manually using ctrl + c or will this affect the model. I used the python -m spacy train config.cfg --output ./output from the beginning so it is supposed to be saving each model.
Here is the process:
"""
============================= Training pipeline =============================
ℹ Pipeline: ['transformer', 'ner']
ℹ Initial learn rate: 0.0
E # LOSS TRANS... LOSS NER ENTS_F ENTS_P ENTS_R SCORE

---

0 0 1067.52 1674.37 0.00 0.00 0.22 0.00
0 200 85731.14 33170.92 71.12 75.67 67.09 0.71
1 400 42521.32 1832.88 86.16 90.75 82.01 0.86
2 600 108414.21 2911.67 89.98 90.49 89.48 0.90
3 800 11231.89 896.99 86.07 80.68 92.24 0.86
3 1000 623.92 605.81 87.16 89.03 85.37 0.87
4 1200 1223.48 676.04 90.20 86.61 94.10 0.90
5 1400 818.14 535.47 89.96 87.73 92.31 0.90
6 1600 171.46 298.32 91.07 95.42 87.09 0.91
6 1800 157.00 327.35 93.06 91.19 95.00 0.93
7 2000 1104.12 333.76 93.00 92.29 93.73 0.93
8 2200 120.79 229.26 93.76 95.58 92.01 0.94
9 2400 399.62 244.94 93.02 91.12 95.00 0.93
10 2600 74.70 176.95 94.35 93.93 94.78 0.94
10 2800 73.17 154.58 94.53 94.35 94.70 0.95
11 3000 72.04 130.66 93.80 93.28 94.33 0.94
12 3200 77.78 164.30 93.35 91.48 95.30 0.93
13 3400 157.12 125.21 93.83 93.42 94.25 0.94
"""
And here is my config file
"""
[paths]
train = "train.spacy"
dev = "dev.spacy"
vectors = null
init_tok2vec = null

[system]
gpu_allocator = "pytorch"
seed = 0

[nlp]
lang = "en"
pipeline = ["transformer","ner"]
batch_size = 128
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
tokenizer = {"@Tokenizers":"spacy.Tokenizer.v1"}
vectors = {"@vectors":"spacy.Vectors.v1"}

[components]

[components.ner]
factory = "ner"
incorrect_spans_key = null
moves = null
scorer = {"@scorers":"spacy.ner_scorer.v1"}
update_with_oracle_cut_size = 100

[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null

[components.ner.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@layers":"reduce_mean.v1"}
upstream = "*"

[components.transformer]
factory = "transformer"
max_batch_items = 4096
set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}

[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "roberta-base"
mixed_precision = false

[components.transformer.model.get_spans]
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96

[components.transformer.model.grad_scaler_config]

[components.transformer.model.tokenizer_config]
use_fast = true

[components.transformer.model.transformer_config]

[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]
output_directory = "roberta7030"
accumulate_gradient = 3
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 200
frozen_components = []
annotating_components = []
before_to_disk = null
before_update = null

[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
get_length = null

[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

[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 0.00005

[training.score_weights]
ents_f = 1.0
ents_p = 0.0
ents_r = 0.0
ents_per_type = null

[pretraining]

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

[initialize.components]

[initialize.tokenizer]
"""