Ir is fine-tuned
DistilBERT-NER model with the classifier replaced to increase the number of classes from 9 to 11. Two additional classes is I-MOU and B-MOU what stands for mountain.
Inital new classifier inherited all weights and biases from original and add new beurons wirh weights initialized wirh xavier_uniform_
1from transformers import AutoTokenizer, AutoModelForTokenClassification
2from transformers import pipeline
3tokenizer = AutoTokenizer.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountains")
4model = AutoModelForTokenClassification.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountains")
5nlp = pipeline("ner", model=model, tokenizer=tokenizer)
6example = "My name is Wolfgang and I live in Berlin"
7ner_results = nlp(example)
8print(ner_results)
This model was fine-tuned on English castom arteficial dataset with sentances wich contains mountains.
As in the dataset, each token will be classified as one of the following classes: