SwanLab官方文档

Appearance

Sentence Transformers ​

Sentence Transformers(又名SBERT)是访问、使用和训练文本和图像嵌入（Embedding）模型的Python库。

你可以使用Sentence Transformers快速进行模型训练，同时使用SwanLab进行实验跟踪与可视化。

1. 引入SwanLabCallback ​

python
from swanlab.integration.huggingface import SwanLabCallback

SwanLabCallback是适配于HuggingFace系列工具（Transformers等）的日志记录类。

SwanLabCallback可以定义的参数有：

  • project、experiment_name、description 等与 swanlab.init 效果一致的参数, 用于SwanLab项目的初始化。
  • 你也可以在外部通过swanlab.init创建项目，集成会将实验记录到你在外部创建的项目中。

2. 传入Trainer ​

python
from swanlab.integration.huggingface import SwanLabCallback
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer

...

# 实例化SwanLabCallback
swanlab_callback = SwanLabCallback(project="hf-visualization")

trainer = SentenceTransformerTrainer(
    ...
    # 传入callbacks参数
    callbacks=[swanlab_callback],
)

trainer.train()

3.完整案例代码 ​

python
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.losses import MultipleNegativesRankingLoss
from swanlab.integration.huggingface import SwanLabCallback

model = SentenceTransformer("bert-base-uncased")

train_dataset = load_dataset("sentence-transformers/all-nli", "pair", split="train[:10000]")
eval_dataset = load_dataset("sentence-transformers/all-nli", "triplet", split="dev")
mnrl_loss = MultipleNegativesRankingLoss(model)

swanlab_callback = SwanLabCallback(project="sentence-transformers", experiment_name="bert-all-nli")

trainer = SentenceTransformerTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss=mnrl_loss,
    callbacks=[swanlab_callback],
)

trainer.train()