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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Mistral-7B-Instruct-v0.2-absa-MT-laptops |
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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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# Mistral-7B-Instruct-v0.2-absa-MT-laptops |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0060 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- training_steps: 1200 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.8786 | 0.13 | 40 | 0.1392 | |
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| 0.0627 | 0.25 | 80 | 0.0165 | |
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| 0.0162 | 0.38 | 120 | 0.0143 | |
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| 0.0139 | 0.5 | 160 | 0.0125 | |
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| 0.0131 | 0.63 | 200 | 0.0110 | |
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| 0.0115 | 0.75 | 240 | 0.0106 | |
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| 0.0111 | 0.88 | 280 | 0.0105 | |
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| 0.0091 | 1.0 | 320 | 0.0093 | |
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| 0.0073 | 1.13 | 360 | 0.0090 | |
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| 0.0079 | 1.25 | 400 | 0.0090 | |
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| 0.0068 | 1.38 | 440 | 0.0083 | |
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| 0.0065 | 1.5 | 480 | 0.0076 | |
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| 0.0071 | 1.63 | 520 | 0.0076 | |
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| 0.0062 | 1.75 | 560 | 0.0077 | |
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| 0.0062 | 1.88 | 600 | 0.0069 | |
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| 0.0058 | 2.0 | 640 | 0.0069 | |
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| 0.0034 | 2.13 | 680 | 0.0070 | |
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| 0.0034 | 2.25 | 720 | 0.0066 | |
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| 0.0034 | 2.38 | 760 | 0.0071 | |
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| 0.0038 | 2.5 | 800 | 0.0064 | |
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| 0.0032 | 2.63 | 840 | 0.0070 | |
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| 0.0031 | 2.75 | 880 | 0.0062 | |
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| 0.0032 | 2.88 | 920 | 0.0058 | |
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| 0.0026 | 3.0 | 960 | 0.0059 | |
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| 0.0018 | 3.13 | 1000 | 0.0058 | |
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| 0.0014 | 3.26 | 1040 | 0.0059 | |
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| 0.0014 | 3.38 | 1080 | 0.0060 | |
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| 0.0012 | 3.51 | 1120 | 0.0060 | |
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| 0.0014 | 3.63 | 1160 | 0.0060 | |
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| 0.001 | 3.76 | 1200 | 0.0060 | |
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### Framework versions |
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- Transformers 4.38.2 |
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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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