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--- |
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license: other |
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base_model: Qwen/Qwen1.5-14B-Chat |
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tags: |
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- llama-factory |
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- full |
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- generated_from_trainer |
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model-index: |
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- name: PsycoLLM |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# PsycoLLM 心理大模型 |
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This model is a fine-tuned version of [Qwen/Qwen1.5-14B-Chat](https://huggingface.co//data/zonepg/models/Qwen/Qwen1.5-14B-Chat) on the QAs, the ds and the dialogue datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3823 |
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## Model description |
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We will open source the entire dataset in the future. Please keep focusing on our work. |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 20 |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.2867 | 0.63 | 100 | 1.2870 | |
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| 0.9624 | 1.27 | 200 | 1.2869 | |
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| 0.9492 | 1.9 | 300 | 1.2718 | |
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| 0.6774 | 2.53 | 400 | 1.3823 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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## Citation |
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If this work is helpful, please kindly cite as: |
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```bibtex |
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@ARTICLE{10772313, |
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author={Hu, Jinpeng and Dong, Tengteng and Luo, Gang and Ma, Hui and Zou, Peng and Sun, Xiao and Guo, Dan and Yang, Xun and Wang, Meng}, |
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journal={IEEE Transactions on Computational Social Systems}, |
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title={PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation}, |
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year={2025}, |
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volume={12}, |
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number={2}, |
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pages={539-551}, |
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keywords={Benchmark testing;Mental health;Law;Electronic mail;Context modeling;Knowledge based systems;Data mining;Transformers;Training;Large language models;Large language model (LLM);mental health;psychological evaluation;psychological understanding}, |
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doi={10.1109/TCSS.2024.3497725}} |
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``` |
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