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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
- matthews_correlation
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: Mistral-7B-v0.1_colaMistral_scratch_cola
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-v0.1_colaMistral_scratch_cola
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4281
- Accuracy: {'accuracy': 0.8387850467289719}
- Matthews Correlation: 0.6114
## 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: 8
- eval_batch_size: 16
- seed: 2
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------:|
| 1.9322 | 0.17 | 20 | 1.5215 | {'accuracy': 0.5743048897411314} | 0.0818 |
| 1.1953 | 0.33 | 40 | 0.9950 | {'accuracy': 0.660594439117929} | 0.1870 |
| 0.6611 | 0.5 | 60 | 0.7549 | {'accuracy': 0.7353787152444871} | 0.3527 |
| 0.6165 | 0.66 | 80 | 0.6317 | {'accuracy': 0.7583892617449665} | 0.4081 |
| 0.5467 | 0.83 | 100 | 0.5667 | {'accuracy': 0.7842761265580057} | 0.5041 |
| 0.4864 | 1.0 | 120 | 0.5268 | {'accuracy': 0.7996164908916586} | 0.5385 |
| 0.478 | 1.16 | 140 | 0.4803 | {'accuracy': 0.8283796740172579} | 0.5859 |
| 0.439 | 1.33 | 160 | 0.4965 | {'accuracy': 0.8293384467881112} | 0.5818 |
| 0.4395 | 1.49 | 180 | 0.4669 | {'accuracy': 0.8283796740172579} | 0.5778 |
| 0.4202 | 1.66 | 200 | 0.5002 | {'accuracy': 0.825503355704698} | 0.6192 |
| 0.3485 | 1.83 | 220 | 0.4360 | {'accuracy': 0.8389261744966443} | 0.6099 |
| 0.442 | 1.99 | 240 | 0.4391 | {'accuracy': 0.840843720038351} | 0.6121 |
| 0.3752 | 2.16 | 260 | 0.4306 | {'accuracy': 0.8446788111217641} | 0.6474 |
| 0.3013 | 2.32 | 280 | 0.4163 | {'accuracy': 0.8427612655800575} | 0.6216 |
| 0.3395 | 2.49 | 300 | 0.4151 | {'accuracy': 0.8542665388302972} | 0.6592 |
| 0.3305 | 2.66 | 320 | 0.4096 | {'accuracy': 0.8475551294343241} | 0.6299 |
| 0.342 | 2.82 | 340 | 0.4101 | {'accuracy': 0.8465963566634708} | 0.6322 |
| 0.3183 | 2.99 | 360 | 0.4166 | {'accuracy': 0.8494726749760306} | 0.6364 |
| 0.2551 | 3.15 | 380 | 0.4321 | {'accuracy': 0.8542665388302972} | 0.6503 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0 |