|
--- |
|
license: apache-2.0 |
|
library_name: peft |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
base_model: mistralai/Mistral-7B-Instruct-v0.1 |
|
model-index: |
|
- name: mistral_instruct_classify10k |
|
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_instruct_classify10k |
|
|
|
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4669 |
|
- F1 Micro: 0.5541 |
|
- F1 Macro: 0.4757 |
|
- Accuracy: 0.8606 |
|
|
|
## 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: 0.0002 |
|
- train_batch_size: 6 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: constant |
|
- lr_scheduler_warmup_steps: 0.03 |
|
- num_epochs: 6 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:| |
|
| 0.5713 | 1.0 | 1345 | 0.5121 | 0.5518 | 0.4780 | 0.8361 | |
|
| 2.1107 | 2.0 | 2690 | 1.0088 | 0.5039 | 0.4158 | 0.7536 | |
|
| 0.7897 | 3.0 | 4035 | 0.8093 | 0.4448 | 0.3756 | 0.6377 | |
|
| 0.2022 | 4.0 | 5380 | 0.3706 | 0.5619 | 0.4837 | 0.8751 | |
|
| 0.4403 | 5.0 | 6725 | 0.4996 | 0.5552 | 0.4811 | 0.8406 | |
|
| 0.3214 | 6.0 | 8070 | 0.4669 | 0.5541 | 0.4757 | 0.8606 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.8.2 |
|
- Transformers 4.37.2 |
|
- Pytorch 2.2.0+cu121 |
|
- Datasets 2.17.0 |
|
- Tokenizers 0.15.2 |
