File size: 2,019 Bytes
661293f 2cdc6c9 661293f c2543c1 2cdc6c9 661293f c2543c1 661293f c2543c1 661293f c2543c1 661293f c2543c1 661293f 2cdc6c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
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 |