| --- |
| license: apache-2.0 |
| base_model: mistralai/Mistral-7B-Instruct-v0.1 |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: original_glue_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. --> |
|
|
| # original_glue_cola |
|
|
| This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3803 |
| - Accuracy: 0.8363 |
|
|
| ## 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: 0 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - gradient_accumulation_steps: 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: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.4253 | 0.22 | 50 | 0.4968 | 0.7553 | |
| | 0.5036 | 0.44 | 100 | 0.4704 | 0.7779 | |
| | 0.4974 | 0.66 | 150 | 0.4562 | 0.7825 | |
| | 0.4649 | 0.88 | 200 | 0.4299 | 0.7880 | |
| | 0.3356 | 1.1 | 250 | 0.4155 | 0.8051 | |
| | 0.4005 | 1.32 | 300 | 0.4026 | 0.8184 | |
| | 0.328 | 1.54 | 350 | 0.4052 | 0.8145 | |
| | 0.3632 | 1.76 | 400 | 0.3889 | 0.8270 | |
| | 0.3334 | 1.98 | 450 | 0.4176 | 0.8036 | |
| | 0.3166 | 2.2 | 500 | 0.4195 | 0.8324 | |
| | 0.2649 | 2.42 | 550 | 0.3929 | 0.8254 | |
| | 0.2805 | 2.64 | 600 | 0.3877 | 0.8363 | |
| | 0.3357 | 2.86 | 650 | 0.3734 | 0.8457 | |
| | 0.2476 | 3.08 | 700 | 0.3930 | 0.8418 | |
| | 0.2361 | 3.3 | 750 | 0.3893 | 0.8566 | |
| | 0.2375 | 3.52 | 800 | 0.3769 | 0.8519 | |
| | 0.18 | 3.74 | 850 | 0.3757 | 0.8519 | |
| | 0.2305 | 3.96 | 900 | 0.3783 | 0.8465 | |
| | 0.1432 | 4.18 | 950 | 0.4876 | 0.8402 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.1+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
