File size: 6,857 Bytes
303e8ac | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_models
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. -->
# finetuned_models
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4359
- Accuracy: 0.9399
- F1: 0.9399
- Precision: 0.9400
- Recall: 0.9399
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.0992 | 0.0865 | 50 | 0.8783 | 0.6714 | 0.6681 | 0.6770 | 0.6714 |
| 0.5328 | 0.1730 | 100 | 0.6396 | 0.7875 | 0.7841 | 0.8010 | 0.7875 |
| 0.3164 | 0.2595 | 150 | 0.5246 | 0.8255 | 0.8237 | 0.8318 | 0.8255 |
| 0.6413 | 0.3460 | 200 | 0.3856 | 0.8792 | 0.8790 | 0.8813 | 0.8792 |
| 0.5142 | 0.4325 | 250 | 0.3306 | 0.8852 | 0.8856 | 0.8880 | 0.8852 |
| 0.4424 | 0.5190 | 300 | 0.3374 | 0.8848 | 0.8854 | 0.8881 | 0.8848 |
| 0.3363 | 0.6055 | 350 | 0.2875 | 0.9089 | 0.9088 | 0.9087 | 0.9089 |
| 0.0819 | 0.6920 | 400 | 0.3526 | 0.8999 | 0.8991 | 0.9027 | 0.8999 |
| 0.2416 | 0.7785 | 450 | 0.2647 | 0.9132 | 0.9135 | 0.9152 | 0.9132 |
| 0.5143 | 0.8651 | 500 | 0.3480 | 0.9042 | 0.9036 | 0.9071 | 0.9042 |
| 0.1396 | 0.9516 | 550 | 0.3365 | 0.8973 | 0.8972 | 0.9032 | 0.8973 |
| 0.101 | 1.0381 | 600 | 0.3081 | 0.9190 | 0.9189 | 0.9216 | 0.9190 |
| 0.1129 | 1.1246 | 650 | 0.3920 | 0.9147 | 0.9154 | 0.9206 | 0.9147 |
| 0.0622 | 1.2111 | 700 | 0.3411 | 0.9275 | 0.9276 | 0.9287 | 0.9275 |
| 0.0808 | 1.2976 | 750 | 0.3357 | 0.9346 | 0.9347 | 0.9348 | 0.9346 |
| 0.0043 | 1.3841 | 800 | 0.3568 | 0.9290 | 0.9292 | 0.9299 | 0.9290 |
| 0.1549 | 1.4706 | 850 | 0.2835 | 0.9328 | 0.9328 | 0.9329 | 0.9328 |
| 0.1464 | 1.5571 | 900 | 0.2998 | 0.9348 | 0.9348 | 0.9351 | 0.9348 |
| 0.193 | 1.6436 | 950 | 0.3660 | 0.9253 | 0.9258 | 0.9284 | 0.9253 |
| 0.2246 | 1.7301 | 1000 | 0.4104 | 0.9281 | 0.9281 | 0.9290 | 0.9281 |
| 0.2503 | 1.8166 | 1050 | 0.3155 | 0.9347 | 0.9348 | 0.9353 | 0.9347 |
| 0.0571 | 1.9031 | 1100 | 0.3476 | 0.9320 | 0.9318 | 0.9322 | 0.9320 |
| 0.034 | 1.9896 | 1150 | 0.3135 | 0.9390 | 0.9389 | 0.9389 | 0.9390 |
| 0.0002 | 2.0761 | 1200 | 0.3508 | 0.9381 | 0.9381 | 0.9381 | 0.9381 |
| 0.0004 | 2.1626 | 1250 | 0.3696 | 0.9381 | 0.9382 | 0.9384 | 0.9381 |
| 0.0022 | 2.2491 | 1300 | 0.3761 | 0.9392 | 0.9392 | 0.9394 | 0.9392 |
| 0.0023 | 2.3356 | 1350 | 0.4013 | 0.9378 | 0.9379 | 0.9385 | 0.9378 |
| 0.0602 | 2.4221 | 1400 | 0.4008 | 0.9384 | 0.9385 | 0.9389 | 0.9384 |
| 0.0095 | 2.5087 | 1450 | 0.4055 | 0.9387 | 0.9388 | 0.9390 | 0.9387 |
| 0.0 | 2.5952 | 1500 | 0.4149 | 0.9390 | 0.9390 | 0.9390 | 0.9390 |
| 0.0 | 2.6817 | 1550 | 0.4279 | 0.9388 | 0.9388 | 0.9388 | 0.9388 |
| 0.0014 | 2.7682 | 1600 | 0.4286 | 0.9397 | 0.9397 | 0.9397 | 0.9397 |
| 0.0002 | 2.8547 | 1650 | 0.4330 | 0.9407 | 0.9407 | 0.9408 | 0.9407 |
| 0.0046 | 2.9412 | 1700 | 0.4357 | 0.9391 | 0.9390 | 0.9391 | 0.9391 |
| 0.0 | 3.0277 | 1750 | 0.4364 | 0.9395 | 0.9395 | 0.9395 | 0.9395 |
| 0.0 | 3.1142 | 1800 | 0.4356 | 0.9398 | 0.9398 | 0.9398 | 0.9398 |
| 0.0 | 3.2007 | 1850 | 0.4367 | 0.9403 | 0.9402 | 0.9403 | 0.9403 |
| 0.0 | 3.2872 | 1900 | 0.4349 | 0.9400 | 0.9400 | 0.9400 | 0.9400 |
| 0.0 | 3.3737 | 1950 | 0.4353 | 0.9399 | 0.9399 | 0.9400 | 0.9399 |
| 0.0 | 3.4602 | 2000 | 0.4349 | 0.9403 | 0.9403 | 0.9403 | 0.9403 |
| 0.0 | 3.5467 | 2050 | 0.4350 | 0.9400 | 0.9400 | 0.9400 | 0.9400 |
| 0.0 | 3.6332 | 2100 | 0.4353 | 0.9397 | 0.9397 | 0.9397 | 0.9397 |
| 0.0 | 3.7197 | 2150 | 0.4358 | 0.9403 | 0.9402 | 0.9402 | 0.9403 |
| 0.0 | 3.8062 | 2200 | 0.4350 | 0.9400 | 0.9400 | 0.9401 | 0.9400 |
| 0.0 | 3.8927 | 2250 | 0.4343 | 0.9397 | 0.9397 | 0.9398 | 0.9397 |
| 0.0 | 3.9792 | 2300 | 0.4333 | 0.9399 | 0.9399 | 0.9400 | 0.9399 |
| 0.0 | 4.0657 | 2350 | 0.4336 | 0.9403 | 0.9403 | 0.9403 | 0.9403 |
| 0.0 | 4.1522 | 2400 | 0.4349 | 0.9403 | 0.9403 | 0.9403 | 0.9403 |
| 0.0 | 4.2388 | 2450 | 0.4348 | 0.9397 | 0.9397 | 0.9398 | 0.9397 |
| 0.0 | 4.3253 | 2500 | 0.4346 | 0.9398 | 0.9398 | 0.9399 | 0.9398 |
| 0.0 | 4.4118 | 2550 | 0.4342 | 0.9398 | 0.9398 | 0.9399 | 0.9398 |
| 0.0 | 4.4983 | 2600 | 0.4357 | 0.9397 | 0.9397 | 0.9398 | 0.9397 |
| 0.0 | 4.5848 | 2650 | 0.4357 | 0.9399 | 0.9399 | 0.9400 | 0.9399 |
| 0.0 | 4.6713 | 2700 | 0.4351 | 0.9397 | 0.9397 | 0.9398 | 0.9397 |
| 0.0 | 4.7578 | 2750 | 0.4350 | 0.9401 | 0.9402 | 0.9402 | 0.9401 |
| 0.0 | 4.8443 | 2800 | 0.4350 | 0.9395 | 0.9395 | 0.9395 | 0.9395 |
| 0.0 | 4.9308 | 2850 | 0.4359 | 0.9399 | 0.9399 | 0.9400 | 0.9399 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|