dimanoid12331/distilbert-NER_finetuned_on_mountains

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_

How to use

This model can be utilized with the Transformers pipeline for NER, similar to the BERT models.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountains")
model = AutoModelForTokenClassification.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountains")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"
ner_results = nlp(example)
print(ner_results)

Training data

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:

Abbreviation	Description
O	Outside of a named entity
B-MISC	Beginning of a miscellaneous entity right after another miscellaneous entity
I-MISC	Miscellaneous entity
B-PER	Beginning of a person’s name right after another person’s name
I-PER	Person’s name
B-ORG	Beginning of an organization right after another organization
I-ORG	organization
B-LOC	Beginning of a location right after another location
I-LOC	Location
B-MOU	Beginning of a Mountain right after another Mountain
I-MOU	Mountain

Sentences	Tokens
216	2783

Eval results

Metric	Score
Loss	0.2035
Precision	0.8536
Recall	0.7906
F1	0.7117
Accuracy	0.7906