jaeyong2
/

gte-multilingual-base-Id-embedding

@@ -1,6 +1,10 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- id
+base_model:
+- Alibaba-NLP/gte-multilingual-base
 ---
 # Model Card for Model ID
 ## Model Details
+## Train
+- H/W : colab A100 40GB
+- Data : jaeyong2/Id-emb-PreView
+```
+model_name = "Alibaba-NLP/gte-multilingual-base"
+dataset = datasets.load_dataset("jaeyong2/Id-emb-PreView")
+train_dataloader = DataLoader(dataset['train'], batch_size=8, shuffle=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name).to(torch.bfloat16)
+triplet_loss = TripletLoss(margin=1.0)
+optimizer = AdamW(model.parameters(), lr=5e-5)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+for epoch in range(3):
+    model.train()
+    total_loss = 0
+    count = 0
+    for batch in tqdm(train_dataloader):
+        optimizer.zero_grad()
+        loss = None
+        for index in range(len(batch["context"])):
+            anchor_encodings = tokenizer([batch["context"][index]], truncation=True, padding="max_length", max_length=4096, return_tensors="pt")
+            positive_encodings = tokenizer([batch["Title"][index]], truncation=True, padding="max_length", max_length=256, return_tensors="pt")
+            negative_encodings = tokenizer([batch["Fake Title"][index]], truncation=True, padding="max_length", max_length=256, return_tensors="pt")
+            anchor_encodings = batch_to_device(anchor_encodings, device)
+            positive_encodings = batch_to_device(positive_encodings, device)
+            negative_encodings = batch_to_device(negative_encodings, device)
+            anchor_output = model(**anchor_encodings)[0][:, 0, :]
+            positive_output = model(**positive_encodings)[0][:, 0, :]
+            negative_output = model(**negative_encodings)[0][:, 0, :]
+            if loss==None:
+                loss = triplet_loss(anchor_output, positive_output, negative_output)
+            else:
+                loss += triplet_loss(anchor_output, positive_output, negative_output)
+        loss /= len(batch["context"])
+        loss.backward()
+        optimizer.step()
+```
 ## Evaluation
+Code :
+```
+import torch
+import numpy as np
+from sklearn.metrics import pairwise_distances
+from tqdm import tqdm
+dataset = datasets.load_dataset("jaeyong2/Id-emb-PreView")
+validation_dataset = dataset["test"].select(range((1000)))
+model.eval()
+def evaluate(validation_dataset):
+    correct_count = 0
+    for item in tqdm(validation_dataset):
+        query_embedding = get_embedding(item["context"], model, tokenizer)
+        document_embedding = get_embedding(item["Title"], model, tokenizer)
+        negative_embedding = get_embedding(item["Fake Title"], model, tokenizer)
+        positive_distances = pairwise_distances(query_embedding.detach().cpu().float().numpy(), document_embedding.detach().cpu().float().numpy(), metric="cosine")
+        negative_distances = pairwise_distances(query_embedding.detach().cpu().float().numpy(), negative_embedding.detach().cpu().float().numpy(), 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 : 0.959
+- jaeyong2/gte-multilingual-base-Id-embedding : 0.99
+### License
+- Alibaba-NLP/gte-multilingual-base : https://choosealicense.com/licenses/apache-2.0/