File size: 2,776 Bytes
52989f6 eb71f2d 52989f6 07cd446 52989f6 eb71f2d 07cd446 52989f6 |
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 |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
metrics:
- precision
- recall
- accuracy
model-index:
- name: multilabel_classification
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. -->
# multilabel_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2810
- F1 Micro: 0.8770
- F1 Macro: 0.7787
- F1 Weighted: 0.8672
- Precision: 0.8702
- Recall: 0.8770
- Accuracy: 0.8770
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 | F1 Micro | F1 Macro | F1 Weighted | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------:|:------:|:--------:|
| No log | 1.0 | 406 | 0.2865 | 0.8643 | 0.7287 | 0.8438 | 0.8620 | 0.8643 | 0.8643 |
| 0.2729 | 2.0 | 812 | 0.2924 | 0.8737 | 0.7671 | 0.8616 | 0.8671 | 0.8737 | 0.8737 |
| 0.216 | 3.0 | 1218 | 0.2810 | 0.8770 | 0.7787 | 0.8672 | 0.8702 | 0.8770 | 0.8770 |
| 0.1868 | 4.0 | 1624 | 0.2813 | 0.8787 | 0.7802 | 0.8685 | 0.8725 | 0.8787 | 0.8787 |
| 0.1728 | 5.0 | 2030 | 0.2944 | 0.8748 | 0.7794 | 0.8664 | 0.8673 | 0.8748 | 0.8748 |
| 0.1728 | 6.0 | 2436 | 0.2937 | 0.8825 | 0.7967 | 0.8760 | 0.8762 | 0.8825 | 0.8825 |
| 0.155 | 7.0 | 2842 | 0.3007 | 0.8848 | 0.8039 | 0.8795 | 0.8789 | 0.8848 | 0.8848 |
| 0.151 | 8.0 | 3248 | 0.3007 | 0.8875 | 0.8070 | 0.8818 | 0.8819 | 0.8875 | 0.8875 |
| 0.1359 | 9.0 | 3654 | 0.3031 | 0.8870 | 0.8077 | 0.8818 | 0.8814 | 0.8870 | 0.8870 |
| 0.1359 | 10.0 | 4060 | 0.3035 | 0.8881 | 0.8086 | 0.8826 | 0.8826 | 0.8881 | 0.8881 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.15.1 |