File size: 3,161 Bytes
cd4b7da | 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 87 | ---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- multi-label text classification
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta_classifier
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. -->
# deberta_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Accuracy: 0.9955
- F1: 0.6062
- Precision: 0.8225
- Recall: 0.4799
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6159 | 0.1169 | 100 | 0.5955 | 0.7621 | 0.0288 | 0.0148 | 0.4839 |
| 0.3536 | 0.2338 | 200 | 0.3085 | 0.9753 | 0.1645 | 0.1091 | 0.3341 |
| 0.1166 | 0.3507 | 300 | 0.0917 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0456 | 0.4676 | 400 | 0.0375 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0308 | 0.5845 | 500 | 0.0270 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0249 | 0.7013 | 600 | 0.0234 | 0.9942 | 0.4459 | 0.7407 | 0.3189 |
| 0.0231 | 0.8182 | 700 | 0.0211 | 0.9953 | 0.5983 | 0.7970 | 0.4789 |
| 0.0213 | 0.9351 | 800 | 0.0196 | 0.9953 | 0.5989 | 0.7998 | 0.4787 |
| 0.0197 | 1.0520 | 900 | 0.0187 | 0.9954 | 0.6029 | 0.8168 | 0.4778 |
| 0.0205 | 1.1689 | 1000 | 0.0183 | 0.9955 | 0.6062 | 0.8225 | 0.4799 |
| 0.017 | 1.2858 | 1100 | 0.0175 | 0.9959 | 0.6610 | 0.8426 | 0.5437 |
| 0.018 | 1.4027 | 1200 | 0.0170 | 0.9960 | 0.6653 | 0.8685 | 0.5392 |
| 0.0177 | 1.5196 | 1300 | 0.0165 | 0.9961 | 0.6722 | 0.8732 | 0.5464 |
| 0.0189 | 1.6365 | 1400 | 0.0162 | 0.9962 | 0.6752 | 0.8910 | 0.5435 |
| 0.0179 | 1.7534 | 1500 | 0.0159 | 0.9964 | 0.6898 | 0.9151 | 0.5535 |
| 0.0169 | 1.8703 | 1600 | 0.0158 | 0.9964 | 0.6928 | 0.9030 | 0.5620 |
| 0.0172 | 1.9871 | 1700 | 0.0156 | 0.9964 | 0.6909 | 0.9130 | 0.5557 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|