What are spaCy Machine Learning models?

import spacy

text = '''
SARS-CoV-2 Omicron led to the most serious outbreak of COVID-19 in Hong Kong in 2022. Under the pressure of a high volume of patients and limited medical resources, Chinese herbal medicine (CHM) has been extensively used. This is a case-control study of the infected patients that aims to evaluate the effectiveness of CHM using data extracted from the Hong Kong Baptist University Telemedicine Chinese Medicine Centre database. Patients with COVID-19 confirmed by either a rapid antigen test or a polymerase chain reaction who had completed two consultations and taken CHM within 10 days of the first positive test were included in the study (CHM group, [Formula: see text]). The matched control cases were those who did not take CHM within 10 days of the first positive test and were based on age ([Formula: see text] 3 years), vaccine doses ([Formula: see text] 3 doses, or 3 doses), and gender (no-CHM group, [Formula: see text]). The outcomes included the negative conversion time (NCT, primary outcome), total score of individual symptoms, number of the reported symptoms, and individual symptom disappearance rates. The NCT of the CHM group (median days: 7.0, interquartile range: 6.0-8.0) was significantly shorter than that of the no-CHM group (8.0, 7.0-10.5; [Formula: see text]). CHM treatment significantly reduced the total score of individual symptoms ([Formula: see text]) and the number of the reported symptoms ([Formula: see text]) as compared with that of the no-CHM group. Additionally, the symptom disappearance rates of symptoms such as chills, cough, sputum, dry throat, itching throat, headache, chest tightness, abdominal pain, diarrhea, and fatigue were significantly higher in the CHM group than in the no-CHM group. In conclusion, CHM intervention can significantly reduce NCT and COVID-19 symptoms. Chinese medicine can be accurately prescribed based on a telemedical consultation. 
'''

model_name = 'en'
nlp = spacy.blank(model_name)
doc = nlp(text)
uniq_ents = set([e.text for e in doc.ents])
ent_str = ' | '.join(uniq_ents)
print(f'Model = {model_name} Num Ents = {len(uniq_ents)} Ents = {ent_str}\n')

model_name = 'en_core_web_sm'
nlp = spacy.load(model_name)
doc = nlp(text)
uniq_ents = set([e.text for e in doc.ents])
ent_str = ' | '.join(uniq_ents)
print(f'Model = {model_name} Num Ents = {len(uniq_ents)} Ents = {ent_str}\n')

model_name = 'en_core_web_trf'
nlp = spacy.load(model_name)
doc = nlp(text)
uniq_ents = set([e.text for e in doc.ents])
ent_str = ' | '.join(uniq_ents)
print(f'Model = {model_name} Num Ents = {len(uniq_ents)} Ents = {ent_str}\n')

model_name = 'en_core_sci_sm'
nlp = spacy.load(model_name)
doc = nlp(text)
uniq_ents = set([e.text for e in doc.ents])
ent_str = ' | '.join(uniq_ents)
print(f'Model = {model_name} Num Ents = {len(uniq_ents)} Ents = {ent_str}\n')

model_name = 'en_ner_bc5cdr_md'
nlp = spacy.load(model_name)
doc = nlp(text)
uniq_ents = set([e.text for e in doc.ents])
ent_str = ' | '.join(uniq_ents)
print(f'Model = {model_name} Num Ents = {len(uniq_ents)} Ents = {ent_str}')

Model = en Num Ents = 0 Ents = 

Model = en_core_web_sm Num Ents = 16 Ents = Omicron | 3 years | CHM | 7.0 | COVID-19 | Formula | 10 days | (median days | 3 | Chinese | two | first | 6.0 | Hong Kong | 8.0 | 2022

Model = en_core_web_trf Num Ents = 15 Ents = 3 years | 7.0 | median days | 6.0-8.0 | 7.0-10.5 | 10 days | 3 | 8.0 | Chinese | two | first | Telemedicine Chinese Medicine Centre | Hong Kong | Hong Kong Baptist University | 2022

Model = en_core_sci_sm Num Ents = 57 Ents = chest tightness | rapid antigen test | medical resources | chills | Formula | diarrhea | total score | symptom disappearance rates | group | symptoms | intervention | infected | NCT | itching throat | Chinese medicine | years | headache | study | patients | Patients | no-CHM | volume | outbreak | case-control study | CHM group | outcomes | gender | score | evaluate | days | dry throat | CHM | individual | polymerase chain reaction | cough | positive test | fatigue | treatment | vaccine doses | effectiveness | pressure | no-CHM group | COVID-19 | interquartile range | negative | telemedical consultation | consultations | age | doses | Chinese herbal medicine | primary outcome | prescribed | data | sputum | Hong Kong | abdominal pain | Hong Kong Baptist University Telemedicine Chinese Medicine Centre database

Model = en_ner_bc5cdr_md Num Ents = 9 Ents = chest tightness | cough | headache | chills | diarrhea | fatigue | throat | abdominal pain | itching throat