chore: import upstream snapshot with attribution

examples/train/seq_cls/regression/deploy.sh

@@ -0,0 +1,8 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift deploy \
+    --adapters output/vx-xxx/checkpoint-xxx
+
+# curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
+# "model": "Qwen2.5-0.5B",
+# "messages": [{"role": "user", "content": "Task: Based on the given two sentences, provide a similarity score between 0.0 and 1.0.\nSentence 1: The animal is eating.\nSentence 2: A woman is dancing.\nSimilarity score: "}]
+# }'

examples/train/seq_cls/regression/infer.sh

@@ -0,0 +1,5 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --load_data_args true \
+    --max_batch_size 16

examples/train/seq_cls/regression/sft.sh

@@ -0,0 +1,30 @@
+# 2GB
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-0.5B \
+    --tuner_type lora \
+    --dataset 'sentence-transformers/stsb:reg#20000' \
+    --load_from_cache_file true \
+    --split_dataset_ratio 0.01 \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 16 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --num_labels 1 \
+    --task_type seq_cls \
+    --use_chat_template false \
+    --problem_type regression