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---
library_name: transformers
license: apache-2.0
datasets:
- jaeyong2/Ja-emb-PreView
language:
- ja
base_model:
- Alibaba-NLP/gte-multilingual-base
---
# 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/Ja-emb-PreView
```
model_name = "Alibaba-NLP/gte-multilingual-base"
dataset = datasets.load_dataset("jaeyong2/Ja-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/Ja-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 : 0.979
- jaeyong2/gte-multilingual-base-Ja-embedding : 0.995
### License
- Alibaba-NLP/gte-multilingual-base : https://choosealicense.com/licenses/apache-2.0/ |