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---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: Qwen/Qwen1.5-7B
metrics:
- accuracy
model-index:
- name: lex_glue_ledgar
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. -->
# lex_glue_ledgar
This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5025
- Accuracy: 0.867
- F1 Macro: 0.7910
- F1 Micro: 0.867
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.7995 | 0.05 | 100 | 1.6894 | 0.6512 | 0.4676 | 0.6512 |
| 1.3922 | 0.11 | 200 | 1.2208 | 0.7076 | 0.5868 | 0.7076 |
| 1.0552 | 0.16 | 300 | 0.9665 | 0.7634 | 0.6329 | 0.7634 |
| 0.8416 | 0.21 | 400 | 0.9615 | 0.767 | 0.6280 | 0.767 |
| 0.8204 | 0.27 | 500 | 0.8469 | 0.7892 | 0.6680 | 0.7892 |
| 0.7359 | 0.32 | 600 | 0.7820 | 0.8025 | 0.6859 | 0.8025 |
| 0.7088 | 0.37 | 700 | 0.7905 | 0.7975 | 0.6808 | 0.7975 |
| 0.6096 | 0.43 | 800 | 0.7862 | 0.8009 | 0.6823 | 0.8009 |
| 0.8682 | 0.48 | 900 | 0.7768 | 0.7987 | 0.6967 | 0.7987 |
| 0.6772 | 0.53 | 1000 | 0.7300 | 0.8094 | 0.6934 | 0.8094 |
| 0.6224 | 0.59 | 1100 | 0.6760 | 0.8146 | 0.7190 | 0.8146 |
| 0.5875 | 0.64 | 1200 | 0.6449 | 0.8253 | 0.7442 | 0.8253 |
| 0.6147 | 0.69 | 1300 | 0.6603 | 0.8305 | 0.7208 | 0.8305 |
| 0.6355 | 0.75 | 1400 | 0.6256 | 0.8285 | 0.7294 | 0.8285 |
| 0.7076 | 0.8 | 1500 | 0.6340 | 0.8288 | 0.7290 | 0.8288 |
| 0.4995 | 0.85 | 1600 | 0.6186 | 0.8315 | 0.7422 | 0.8315 |
| 0.5754 | 0.91 | 1700 | 0.6105 | 0.8402 | 0.7482 | 0.8402 |
| 0.6775 | 0.96 | 1800 | 0.5947 | 0.8369 | 0.7531 | 0.8369 |
| 0.3267 | 1.01 | 1900 | 0.5678 | 0.8528 | 0.7704 | 0.8528 |
| 0.2022 | 1.07 | 2000 | 0.6361 | 0.844 | 0.7639 | 0.844 |
| 0.3831 | 1.12 | 2100 | 0.5957 | 0.8503 | 0.7672 | 0.8503 |
| 0.3235 | 1.17 | 2200 | 0.6062 | 0.8476 | 0.7685 | 0.8476 |
| 0.2279 | 1.23 | 2300 | 0.6255 | 0.847 | 0.7658 | 0.847 |
| 0.3224 | 1.28 | 2400 | 0.5754 | 0.8537 | 0.7772 | 0.8537 |
| 0.3281 | 1.33 | 2500 | 0.5763 | 0.8598 | 0.7769 | 0.8598 |
| 0.3909 | 1.39 | 2600 | 0.5519 | 0.8545 | 0.7778 | 0.8545 |
| 0.3064 | 1.44 | 2700 | 0.5842 | 0.8536 | 0.7790 | 0.8536 |
| 0.2333 | 1.49 | 2800 | 0.6084 | 0.8447 | 0.7674 | 0.8447 |
| 0.2361 | 1.55 | 2900 | 0.5975 | 0.8588 | 0.7853 | 0.8588 |
| 0.3415 | 1.6 | 3000 | 0.5701 | 0.8572 | 0.7844 | 0.8572 |
| 0.2535 | 1.65 | 3100 | 0.5557 | 0.8618 | 0.7828 | 0.8618 |
| 0.2356 | 1.71 | 3200 | 0.5242 | 0.8612 | 0.7822 | 0.8612 |
| 0.3383 | 1.76 | 3300 | 0.5250 | 0.8553 | 0.7873 | 0.8553 |
| 0.1886 | 1.81 | 3400 | 0.5301 | 0.8658 | 0.7924 | 0.8658 |
| 0.2468 | 1.87 | 3500 | 0.5459 | 0.8595 | 0.7813 | 0.8595 |
| 0.2947 | 1.92 | 3600 | 0.5141 | 0.8688 | 0.7910 | 0.8688 |
| 0.2625 | 1.97 | 3700 | 0.5025 | 0.867 | 0.7910 | 0.867 |
| 0.0829 | 2.03 | 3800 | 0.5625 | 0.8697 | 0.8004 | 0.8697 |
| 0.0297 | 2.08 | 3900 | 0.6303 | 0.8698 | 0.8018 | 0.8698 |
| 0.0474 | 2.13 | 4000 | 0.6244 | 0.8713 | 0.8046 | 0.8713 |
| 0.0267 | 2.19 | 4100 | 0.5801 | 0.8737 | 0.8061 | 0.8737 |
| 0.0487 | 2.24 | 4200 | 0.5915 | 0.8745 | 0.8018 | 0.8745 |
| 0.0272 | 2.29 | 4300 | 0.6174 | 0.8764 | 0.8043 | 0.8764 |
| 0.02 | 2.35 | 4400 | 0.6261 | 0.87 | 0.7986 | 0.87 |
| 0.0414 | 2.4 | 4500 | 0.6157 | 0.8748 | 0.8036 | 0.8748 |
| 0.0394 | 2.45 | 4600 | 0.6051 | 0.8755 | 0.8076 | 0.8755 |
| 0.0513 | 2.51 | 4700 | 0.6078 | 0.874 | 0.8072 | 0.874 |
| 0.0553 | 2.56 | 4800 | 0.6021 | 0.8734 | 0.8023 | 0.8734 |
| 0.0843 | 2.61 | 4900 | 0.6084 | 0.8766 | 0.8096 | 0.8766 |
| 0.0361 | 2.67 | 5000 | 0.6129 | 0.8764 | 0.8091 | 0.8764 |
| 0.0485 | 2.72 | 5100 | 0.6214 | 0.8789 | 0.8096 | 0.8789 |
| 0.0209 | 2.77 | 5200 | 0.5887 | 0.8795 | 0.8102 | 0.8795 |
| 0.028 | 2.83 | 5300 | 0.5953 | 0.8798 | 0.8132 | 0.8798 |
| 0.0513 | 2.88 | 5400 | 0.5944 | 0.8818 | 0.8154 | 0.8818 |
| 0.0073 | 2.93 | 5500 | 0.6021 | 0.8794 | 0.8136 | 0.8794 |
| 0.0398 | 2.99 | 5600 | 0.6064 | 0.88 | 0.8124 | 0.88 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |