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Mistral-7B-Instruct-v0.2-absa-MT-laptops
Shakhovak/Mistral-7B-Instruct-v0.2-absa-MT-laptops
Mistral-7B-Instruct-v0.2-absa-MT-laptops is a machine learning model from Shakhovak. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Updated Apr 27, 2024
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Apr 26, 2024
- Updated
- Apr 27, 2024
- SHA
- 91680293
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Mistral-7B-Instruct-v0.2-absa-MT-laptops
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0060
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 1200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8786 | 0.13 | 40 | 0.1392 |
| 0.0627 | 0.25 | 80 | 0.0165 |
| 0.0162 | 0.38 | 120 | 0.0143 |
| 0.0139 | 0.5 | 160 | 0.0125 |
| 0.0131 | 0.63 | 200 | 0.0110 |
| 0.0115 | 0.75 | 240 | 0.0106 |
| 0.0111 | 0.88 | 280 | 0.0105 |
| 0.0091 | 1.0 | 320 | 0.0093 |
| 0.0073 | 1.13 | 360 | 0.0090 |
| 0.0079 | 1.25 | 400 | 0.0090 |
| 0.0068 | 1.38 | 440 | 0.0083 |
| 0.0065 | 1.5 | 480 | 0.0076 |
| 0.0071 | 1.63 | 520 | 0.0076 |
| 0.0062 | 1.75 | 560 | 0.0077 |
| 0.0062 | 1.88 | 600 | 0.0069 |
| 0.0058 | 2.0 | 640 | 0.0069 |
| 0.0034 | 2.13 | 680 | 0.0070 |
| 0.0034 | 2.25 | 720 | 0.0066 |
| 0.0034 | 2.38 | 760 | 0.0071 |
| 0.0038 | 2.5 | 800 | 0.0064 |
| 0.0032 | 2.63 | 840 | 0.0070 |
| 0.0031 | 2.75 | 880 | 0.0062 |
| 0.0032 | 2.88 | 920 | 0.0058 |
| 0.0026 | 3.0 | 960 | 0.0059 |
| 0.0018 | 3.13 | 1000 | 0.0058 |
| 0.0014 | 3.26 | 1040 | 0.0059 |
| 0.0014 | 3.38 | 1080 | 0.0060 |
| 0.0012 | 3.51 | 1120 | 0.0060 |
| 0.0014 | 3.63 | 1160 | 0.0060 |
| 0.001 | 3.76 | 1200 | 0.0060 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- License
- apache-2.0
- Created
- Apr 26, 2024
- Updated
- Apr 27, 2024