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SMM4H2024_Task2b_ja

Text Classification
Transformers
Safetensors
Japanese
bert
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metadata
license: afl-3.0
language:
  - ja
library_name: transformers
pipeline_tag: text-classification

SMM4H-2024 Task 2 Japanese RE

Overview

This is a relation extraction model created by fine-tuning daisaku-s/medtxt_ner_roberta on SMM4H 2024 Task 2b corpus.

Tag set:

  • CAUSED
  • TREATMENT_FOR

Usage 

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

text = "サンプルテキスト"
model_name = "yseop/SMM4H2024_Task2b_ja"
id2label = ['O', 'CAUSED', 'TREATMENT_FOR']

with torch.inference_mode():
    model = AutoModelForSequenceClassification.from_pretrained(model_name).eval()
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    encoded_input = tokenizer(text, return_tensors='pt', max_length=512)
    output = re_model(**encoded_input).logits
    class_id = output.argmax().item()
    print(id2label[class_id])

Results

Relation tp fp fn precision recall f1
CAUSED|DISORDER|DISORDER 1 163 38 0.0061 0.0256 0.0099
CAUSED|DISORDER|FUNCTION 0 70 13 0 0 0
CAUSED|DRUG|DISORDER 9 196 105 0.0439 0.0789 0.0564
CAUSED|DRUG|FUNCTION 2 59 7 0.0328 0.2222 0.0571
TREATMENT_FOR|DISORDER|DISORDER 0 12 0 0 0 0
TREATMENT_FOR|DISORDER|FUNCTION 0 3 0 0 0 0
TREATMENT_FOR|DRUG|DISORDER 0 15 91 0 0 0
TREATMENT_FOR|DRUG|FUNCTION 0 0 1 0 0 0
all 12 518 255 0.0226 0.0449 0.0301