metadata
tags:
- transformers
- token-classification
- ner
- bert
- peft
- lora
- conll2003
license: apache-2.0
datasets:
- conll2003
language:
- en
pipeline_tag: token-classification
authors:
- Karan D Vasa (https://huggingface.co/starkdv123)
BERT (base-cased) for CoNLL-2003 NER — LoRA Adapter (PEFT)
This repository contains LoRA adapter weights trained on CoNLL-2003 for BERT base cased.
📊 Reference result (merged model from same adapter)
- Entity Macro F1: 0.9052
Usage (attach adapter)
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
from peft import PeftModel
base = "bert-base-cased"
adapter = "starkdv123/conll2003-bert-ner-lora"
tok = AutoTokenizer.from_pretrained(base)
base_model = AutoModelForTokenClassification.from_pretrained(base, num_labels=9)
model = PeftModel.from_pretrained(base_model, adapter)
clf = pipeline("token-classification", model=model, tokenizer=tok, aggregation_strategy="simple")
clf("Chris Hoiles hit his 22nd homer for Baltimore.")
Training summary
- LoRA: r=8, alpha=16, dropout=0.1
- Targets: [query, key, value, output.dense]
- Epochs: 3, LR: 2e-4, warmup 0.1, batch 16/32
Confusion Matrix
LOC MISC O ORG PER
LOC 384 6 35 43 5
MISC 12 2138 80 100 33
O 57 119 38060 58 21
ORG 43 109 36 2304 11
PER 1 27 18 22 2705