首先,创建zero-shot-classification pipeline。model除了可以运行时拉去,也可以替换为模型本地路径。 from transformers import pipeline classifier = pipeline("zero-shot-classification", model="knowledgator/comprehend_it-base") 定义文本
Traditional product classification methods, which depend heavily on labeled data and manual effort, struggle with scalability and adaptability to diverse product categories. This study explores the transformative potential of large language models (LLMs) for zero-shot product classification in e-commerce,...
对于文本分类来说,ChatGPT 式的生成模型,就是目前阶段效果最拔群的 zero-shot 范式了。但是,大模型再美好,在部署成本和结果优化方面,还是有些门槛的。对于普通业务来说,用 LLM(Large Language Model)辅助小模型训练,可能是短期内更便于执行的落地方案。 在模型冷启动阶段,由于缺乏标注数据,提升 zero-shot 方案的...
, "parameters": { "candidate_labels": classification_categories, "multi_label": False } } Next, you can invoke a SageMaker endpoint with the zero-shot payload. The SageMaker endpoint is deployed as part of the SageMaker JumpStart solution. response = runtime.invoke_endpoint(Endp...
For the large language model (LLM) classifier, OpenAI’s GPT 3.5 (a pre-trained model) was accessed via scikit-llm Python package (https://pypi.org/project/scikit-llm/; the zero-shot GPT classifier and multi-class zero-shot GPT classifiers were used, respectively, for one classification at...
To address these challenges, we propose combining fine-tuning of Large Language Models (LLMs) with knowledge augmentation for zero-shot stance detection. Specifically, we leverage stance detection and related tasks from debate corpora to perform multi-task fine-tuning of LLMs. This approach aims ...
Text Classification (Binary, MultiClass) English (EN), Simplifed Chinese (ZH_CN) 👌 MultiLabel Classification English (EN), Simplifed Chinese (ZH_CN) 👌 Data Augmentation English (EN), Simplifed Chinese (ZH_CN) 👌 Relation Extraction English (EN), Simplifed Chinese (ZH_CN) 👌 Summariza...
--label_len 48 \ --pred_len 192 \ --factor 3 \ --enc_in 7 \ --dec_in 7 \ --c_out 7 \ --des 'Exp' \ --itr 1 \ --d_model 32 \ --d_ff 128 \ --batch_size $batch_size \ --learning_rate 0.02 \ --llm_layers $llama_layers \ --train_epochs 5 \ --model_commen...
Zero-shot 20623 learning [65, 67, 70] can generalize across new categories by leveraging the pre-trained capabilities of CLIP [41] in the 3D domain. LLMs [34, 47, 49] can facilitate 3DVG due to their strong planning and reasoning capabilities. Regard- ing...
ImageNet-S 上具有不同 alpha map level的Zero-shot classification。**当foreground mask不可用时,Alpha-CLIP 与原始 CLIP 相当,并通过矩形框或mask alpha maps进一步提升了性能. Alpha-CLIP in MLLM 我们用 Alpha-CLIP 替换 BLIP-2 和 LLaVA-1.5 中使用的 CLIP,使 MLLM 直接关注视觉语言任务中的用户定义...