File size: 3,332 Bytes
ba0f52a f57543e ba0f52a 9c38b18 ba0f52a 49fbc4e ba0f52a 8d9ea3a 991a300 ba0f52a 991a300 ba0f52a 9c38b18 49fbc4e ba0f52a |
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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
license: mit
tags:
- generated_from_trainer
base_model: Microsoft/Multilingual-MiniLM-L12-H384
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my-model-MiniLM-Area
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. -->
# my-model-MiniLM-Area
This model is a fine-tuned version of [Microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/Microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5228
- Accuracy: 0.4323
- F1: 0.3979
- Precision: 0.3932
- Recall: 0.4323
## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 5
- seed: 42
- 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 | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.8812 | 1.0 | 25 | 1.8038 | 0.2839 | 0.1709 | 0.2712 | 0.2839 |
| 1.8043 | 2.0 | 50 | 1.7540 | 0.3742 | 0.2586 | 0.2046 | 0.3742 |
| 1.7687 | 3.0 | 75 | 1.6908 | 0.3806 | 0.2557 | 0.1927 | 0.3806 |
| 1.6959 | 4.0 | 100 | 1.6325 | 0.4 | 0.2695 | 0.2033 | 0.4 |
| 1.6178 | 5.0 | 125 | 1.6401 | 0.4129 | 0.3338 | 0.2874 | 0.4129 |
| 1.5189 | 6.0 | 150 | 1.5471 | 0.4581 | 0.3631 | 0.3030 | 0.4581 |
| 1.4393 | 7.0 | 175 | 1.5966 | 0.4258 | 0.3761 | 0.3451 | 0.4258 |
| 1.3757 | 8.0 | 200 | 1.5716 | 0.4452 | 0.3945 | 0.3556 | 0.4452 |
| 1.3032 | 9.0 | 225 | 1.5691 | 0.4387 | 0.3646 | 0.3443 | 0.4387 |
| 1.2434 | 10.0 | 250 | 1.5740 | 0.4452 | 0.4057 | 0.3798 | 0.4452 |
| 1.1837 | 11.0 | 275 | 1.5108 | 0.4645 | 0.3854 | 0.3852 | 0.4645 |
| 1.1231 | 12.0 | 300 | 1.5409 | 0.4516 | 0.3972 | 0.3561 | 0.4516 |
| 1.0815 | 13.0 | 325 | 1.5111 | 0.4774 | 0.4116 | 0.3865 | 0.4774 |
| 1.0555 | 14.0 | 350 | 1.5171 | 0.4645 | 0.4014 | 0.3674 | 0.4645 |
| 0.9964 | 15.0 | 375 | 1.4971 | 0.4581 | 0.3877 | 0.3504 | 0.4581 |
| 0.9627 | 16.0 | 400 | 1.5157 | 0.4516 | 0.4118 | 0.3882 | 0.4516 |
| 0.9247 | 17.0 | 425 | 1.4996 | 0.4387 | 0.3882 | 0.3664 | 0.4387 |
| 0.9286 | 18.0 | 450 | 1.4990 | 0.4452 | 0.4008 | 0.3856 | 0.4452 |
| 0.892 | 19.0 | 475 | 1.5288 | 0.4323 | 0.4025 | 0.4031 | 0.4323 |
| 0.8843 | 20.0 | 500 | 1.5228 | 0.4323 | 0.3979 | 0.3932 | 0.4323 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|