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---
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_Fact_U
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_Fact_U
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: 1.6060
- Accuracy: 0.7941
- Precision: 0.8486
- Recall: 0.7353
- F1 score: 0.7879
## 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: 4
- 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 |
|:-------------:|:------:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:|
| 1.4759 | 0.2509 | 200 | 0.6494 | 0.6674 | 0.6586 | 0.6765 | 1.2749 |
| 1.033 | 0.5019 | 400 | 0.6682 | 0.5841 | 0.8390 | 0.4480 | 1.1062 |
| 0.8323 | 0.7528 | 600 | 0.7094 | 0.7487 | 0.6802 | 0.8326 | 0.6781 |
| 0.6895 | 1.0038 | 800 | 0.9914 | 0.7529 | 0.9113 | 0.5814 | 0.7099 |
| 0.5692 | 1.2547 | 1000 | 0.5985 | 0.7741 | 0.7962 | 0.7602 | 0.7778 |
| 0.5514 | 1.5056 | 1200 | 0.6206 | 0.7259 | 0.6961 | 0.8394 | 0.7610 |
| 0.5011 | 1.7566 | 1400 | 0.5612 | 0.7835 | 0.8377 | 0.7240 | 0.7767 |
| 0.4772 | 2.0075 | 1600 | 0.7216 | 0.7482 | 0.7446 | 0.7851 | 0.7643 |
| 0.4001 | 2.2585 | 1800 | 0.8057 | 0.7106 | 0.6782 | 0.8439 | 0.7520 |
| 0.3915 | 2.5094 | 2000 | 0.6729 | 0.7706 | 0.7800 | 0.7783 | 0.7792 |
| 0.3691 | 2.7604 | 2200 | 0.6833 | 0.7624 | 0.7679 | 0.7783 | 0.7730 |
| 0.387 | 3.0113 | 2400 | 0.6916 | 0.7529 | 0.7489 | 0.7896 | 0.7687 |
| 0.2261 | 3.2622 | 2600 | 1.2210 | 0.7988 | 0.8575 | 0.7353 | 0.7917 |
| 0.236 | 3.5132 | 2800 | 1.0130 | 0.7965 | 0.8645 | 0.7217 | 0.7867 |
| 0.2332 | 3.7641 | 3000 | 1.3376 | 0.7412 | 0.7229 | 0.8145 | 0.7660 |
| 0.2334 | 4.0151 | 3200 | 1.2199 | 0.7847 | 0.8381 | 0.7262 | 0.7782 |
| 0.1039 | 4.2660 | 3400 | 1.4260 | 0.7941 | 0.8380 | 0.7489 | 0.7909 |
| 0.0987 | 4.5169 | 3600 | 1.5102 | 0.7706 | 0.7839 | 0.7715 | 0.7777 |
| 0.0887 | 4.7679 | 3800 | 1.6060 | 0.7941 | 0.8486 | 0.7353 | 0.7879 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |