File size: 3,362 Bytes
29827fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
metrics:
- accuracy
- precision
- recall
model-index:
- name: Mistral_7B_MT
results: []
---
<!-- 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_MT
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8388
- Accuracy: 0.8167
- Precision: 0.8519
- Recall: 0.7667
- F1 score: 0.8070
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 score | Precision | Recall | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:|
| 1.687 | 0.25 | 200 | 0.6233 | 0.4378 | 0.8627 | 0.2933 | 2.0030 |
| 0.9482 | 0.5 | 400 | 0.68 | 0.5616 | 0.8913 | 0.41 | 1.4557 |
| 0.9232 | 0.75 | 600 | 0.72 | 0.6471 | 0.875 | 0.5133 | 0.8805 |
| 0.7781 | 1.0 | 800 | 0.57 | 0.3246 | 0.7561 | 0.2067 | 1.4515 |
| 0.5468 | 1.25 | 1000 | 0.7233 | 0.6483 | 0.8895 | 0.51 | 0.8474 |
| 0.5549 | 1.5 | 1200 | 0.7767 | 0.7403 | 0.8843 | 0.6367 | 0.7168 |
| 0.4883 | 1.75 | 1400 | 0.8 | 0.7719 | 0.8982 | 0.6767 | 0.6943 |
| 0.4639 | 2.0 | 1600 | 0.7767 | 0.7276 | 0.9323 | 0.5967 | 0.7637 |
| 0.3804 | 2.25 | 1800 | 0.7617 | 0.7146 | 0.8905 | 0.5967 | 0.8467 |
| 0.3847 | 2.5 | 2000 | 0.81 | 0.7942 | 0.8661 | 0.7333 | 0.6699 |
| 0.346 | 2.75 | 2200 | 0.7833 | 0.7575 | 0.8602 | 0.6767 | 0.8569 |
| 0.3488 | 3.0 | 2400 | 0.7824 | 0.815 | 0.9238 | 0.6867 | 0.7878 |
| 0.2654 | 3.25 | 2600 | 1.0799 | 0.7683 | 0.9259 | 0.5833 | 0.7157 |
| 0.2506 | 3.5 | 2800 | 0.8567 | 0.8033 | 0.9062 | 0.6767 | 0.7748 |
| 0.2574 | 3.75 | 3000 | 0.7490 | 0.8083 | 0.7846 | 0.85 | 0.816 |
| 0.2137 | 4.0 | 3200 | 0.7665 | 0.8333 | 0.8546 | 0.8033 | 0.8282 |
| 0.1335 | 4.25 | 3400 | 0.8591 | 0.8133 | 0.8013 | 0.8333 | 0.8170 |
| 0.1486 | 4.5 | 3600 | 0.9781 | 0.83 | 0.9091 | 0.7333 | 0.8118 |
| 0.126 | 4.75 | 3800 | 0.8723 | 0.8217 | 0.8642 | 0.7633 | 0.8106 |
| 0.1474 | 5.0 | 4000 | 0.8388 | 0.8167 | 0.8519 | 0.7667 | 0.8070 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |