Update README.md

README.md

@@ -4,4 +4,90 @@ language:
 - kg
 base_model:
 - BAAI/bge-m3
----
+---
+
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+
+## Train
+
+- H/W : colab A100 40GB
+- Data : jaeyong2/Ko-emb-PreView (step : 18000)
+
+```
+!torchrun --nproc_per_node 1 \
+-m FlagEmbedding.finetune.embedder.encoder_only.m3 \
+--output_dir "/content/drive/My Drive/bge_ko.pth" \
+--model_name_or_path BAAI/bge-m3 \
+--train_data ./train.jsonl \
+--learning_rate 1e-5 \
+--bf16 \
+--num_train_epochs 1 \
+--per_device_train_batch_size 1 \
+--dataloader_drop_last True \
+--temperature 0.02 \
+--query_max_len 2048 \
+--passage_max_len 512 \
+--train_group_size 2 \
+--negatives_cross_device \
+--logging_steps 10 \
+--save_steps 1000 \
+--query_instruction_for_retrieval ""
+```
+
+## Evaluation
+
+Code : 
+```
+import torch
+import numpy as np
+from sklearn.metrics import pairwise_distances
+from tqdm import tqdm
+import datasets
+def get_embedding(text, model):
+    with torch.no_grad():
+        embedding = model.encode(text)['dense_vecs']
+    return embedding
+
+
+dataset = datasets.load_dataset("jaeyong2/Ko-emb-PreView")
+validation_dataset = dataset["test"].select(range((1000)))
+
+
+def evaluate(validation_dataset):
+    correct_count = 0
+
+    for item in tqdm(validation_dataset):
+        query_embedding = get_embedding(item["context"], fine_tuned_model)
+        document_embedding = get_embedding(item["Title"], fine_tuned_model)
+        negative_embedding = get_embedding(item["Fake Title"], fine_tuned_model)
+      
+
+        # 쿼리와 모든 문서 간의 유사도 계산 (코사인 거리 사용)
+        positive_distances = pairwise_distances(query_embedding.reshape(1, -1), document_embedding.reshape(1, -1), metric="cosine")
+        negative_distances = pairwise_distances(query_embedding.reshape(1, -1), negative_embedding.reshape(1, -1), metric="cosine")
+
+        if positive_distances < negative_distances:
+            correct_count += 1
+
+    accuracy = correct_count / len(validation_dataset)
+    return accuracy
+
+results = evaluate(validation_dataset)
+print(f"Validation Results: {results}")
+```
+
+Accuracy
+- Alibaba-NLP/gte-multilingual-base : 0.971
+- jaeyong2/gte-multilingual-base-Ko-embedding : 0.992
+
+
+### License
+- Alibaba-NLP/gte-multilingual-base : https://choosealicense.com/licenses/apache-2.0/