init: model card

README.md

@@ -1,3 +1,49 @@
----
-license: afl-3.0
----
+---
+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](https://huggingface.co/daisaku-s/medtxt_ner_roberta) on [SMM4H 2024 Task 2b](https://healthlanguageprocessing.org/smm4h-2024/) corpus.
+
+Tag set:
+* CAUSED
+* TREATMENT_FOR
+
+## Usage
+
+```python
+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|