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huggingface -> transformers
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‎en/examples/bert.md‎

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@@ -45,7 +45,7 @@ The IMDB dataset labels 1 as positive and 0 as negative.
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import torch
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from datasets import load_dataset
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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import swanlab
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def predict(text, model, tokenizer, CLASS_NAME):

‎en/examples/pretrain_llm.md‎

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Use the built-in training from Transformers and integrate SwanLab for visualization and logging:
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```python
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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trainer = transformers.Trainer(
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model=model,
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tokenizer=tokenizer,
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import datasets
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import transformers
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import swanlab
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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import modelscope
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def main():

‎en/examples/qwen_finetune.md‎

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import torch
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from datasets import Dataset
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from modelscope import snapshot_download, AutoTokenizer
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from peft import LoraConfig, TaskType, get_peft_model
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from transformers import AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq
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import os

‎en/guide_cloud/integration/integration-huggingface-transformers.md‎

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@@ -11,7 +11,7 @@ You can use Transformers to quickly train models while using SwanLab for experim
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## 1. Import SwanLabCallback
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```python
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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```
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**SwanLabCallback** is a logging class adapted for Transformers.
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## 2. Pass to Trainer
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```python (1,7,12)
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from transformers import Trainer, TrainingArguments
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...
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import evaluate
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import numpy as np
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import swanlab
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from datasets import load_dataset
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
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‎en/guide_cloud/integration/integration-sentence-transformers.md‎

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## 1. Import SwanLabCallback
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```python
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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```
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**SwanLabCallback** is a logging class adapted for HuggingFace series tools (such as Transformers).
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## 2. Pass to Trainer
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```python (1,7,12)
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
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...
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from datasets import load_dataset
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from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
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from sentence_transformers.losses import MultipleNegativesRankingLoss
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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model = SentenceTransformer("bert-base-uncased")
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‎en/guide_cloud/integration/integration-unsloth.md‎

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## 2. Passing to Trainer
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```python {1,7,12}
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from trl import GRPOTrainer
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...

‎zh/examples/bert.md‎

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import torch
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from datasets import load_dataset
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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import swanlab
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def predict(text, model, tokenizer, CLASS_NAME):

‎zh/examples/pretrain_llm.md‎

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使用transformers自带的train开始训练，并且引入swanlab作为可视化日志记录
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```python
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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trainer = transformers.Trainer(
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model=model,
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tokenizer=tokenizer,
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import datasets
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import transformers
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import swanlab
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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import modelscope
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def main():

‎zh/examples/qwen_finetune.md‎

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import torch
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from datasets import Dataset
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from modelscope import snapshot_download, AutoTokenizer
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from peft import LoraConfig, TaskType, get_peft_model
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from transformers import AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq
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import os

‎zh/guide_cloud/integration/integration-huggingface-transformers.md‎

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## 1. 引入SwanLabCallback
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```python
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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```
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**SwanLabCallback**是适配于Transformers的日志记录类。
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## 2. 传入Trainer
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```python (1,7,12)
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from transformers import Trainer, TrainingArguments
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...
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import evaluate
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import numpy as np
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import swanlab
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from swanlab.integration.huggingface import SwanLabCallback
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from swanlab.integration.transformers import SwanLabCallback
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from datasets import load_dataset
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
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