# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
model = AutoModelForTokenClassification.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")Quick Links
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Check out the documentation for more information.
Part-of-Speech (PoS) Tags
Below are the Part-of-Speech (PoS) tags used in the model:
| Tag | Meaning | Examples |
|---|---|---|
| ADP | Adposition (prepositions or postpositions) | in, on, by |
| ADJ | Adjective | significant, global |
| ADV | Adverb | quickly, often |
| AUX | Auxiliary verb | is, was |
| CCONJ | Coordinating conjunction | and, but |
| DET | Determiner | the, a |
| INTJ | Interjection | oh, wow |
| NOUN | Noun | man, city |
| NUM | Number | one, 2022 |
| PART | Particle | 's, to |
| PRON | Pronoun | he, which |
| PROPN | Proper noun | Neil Armstrong, Paris |
| PUNCT | Punctuation mark | ,, . |
| SCONJ | Subordinating conjunction | because, although |
| SYM | Symbol | $, % |
| VERB | Verb | run, is |
| X | Other (generally words that do not fit into other categories) | [not defined] |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vblagoje/bert-english-uncased-finetuned-pos")