|
--- |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
- f1 |
|
library_name: transformers |
|
pipeline_tag: token-classification |
|
tags: |
|
- deberta-v3 |
|
datasets: |
|
- DFKI-SLT/few-nerd |
|
license: mit |
|
--- |
|
|
|
## Deberta for Named Entity Recognition |
|
|
|
I used a Pretrained Deberta-v3-base and finetuned it on Few-NERD, A NER dataset that contains over 180k examples and over 4.6 million tokens. |
|
|
|
The Token labels are Person, Organisation, Location, Building, Event, Product, Art & Misc. |
|
|
|
## How to use the model |
|
|
|
```python |
|
from transformers import pipeline |
|
|
|
def print_ner(sentences): |
|
"""Cleaning and printing NER results |
|
|
|
""" |
|
for sentence in sentences: |
|
last_entity_type = sentence[0]['entity'] |
|
last_index = sentence[0]['index'] |
|
word = sentence[0]['word'] |
|
for i, token in enumerate(sentence): |
|
if (i > 0): |
|
if (token['entity'] == last_entity_type) and (token['index'] == last_index + 1): |
|
word = word + '' + token['word'] |
|
|
|
else: |
|
word = word.replace('▁', ' ') |
|
print(f"{word[1:]} {last_entity_type}") |
|
word = token['word'] |
|
last_entity_type = token['entity'] |
|
last_index = token['index'] |
|
|
|
if i == len(sentence) - 1: |
|
word = word.replace('▁', ' ') |
|
print(f"{word[1:]} {last_entity_type}") |
|
|
|
|
|
pipe = pipeline(model='RashidNLP/NER-Deberta') |
|
sentence = pipe(["Elon Musk will be at SpaceX's Starbase facility in Boca Chica for the orbital launch of starship next month"]) |
|
print_ner(sentence) |
|
|
|
``` |
