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 mountine. 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_mountines")
model = AutoModelForTokenClassification.from_pretrained("dimanoid12331/distilbert-NER_finetuned_on_mountines")
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 |
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