File size: 2,651 Bytes
5213d32 f5b5fc2 5213d32 f5b5fc2 5213d32 f5b5fc2 5213d32 f5b5fc2 5213d32 |
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 88 89 90 |
---
language:
- en
license: mit
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
base_model: microsoft/deberta-v2-xxlarge
datasets:
- DandinPower/review_onlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v2-xxlarge-otat-recommened-hp
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: DandinPower/review_onlytitleandtext
type: DandinPower/review_onlytitleandtext
metrics:
- type: accuracy
value: 0.6741428571428572
name: Accuracy
---
<!-- 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-v2-xxlarge-otat-recommened-hp
This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7864
- Accuracy: 0.6741
- Macro F1: 0.6719
## 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: 3e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.9641 | 0.46 | 200 | 0.8451 | 0.6327 | 0.6341 |
| 0.8263 | 0.91 | 400 | 0.7768 | 0.6651 | 0.6650 |
| 0.7605 | 1.37 | 600 | 0.7842 | 0.667 | 0.6667 |
| 0.7496 | 1.83 | 800 | 0.7790 | 0.6659 | 0.6650 |
| 0.7034 | 2.29 | 1000 | 0.7738 | 0.67 | 0.6639 |
| 0.7134 | 2.74 | 1200 | 0.7671 | 0.6694 | 0.6698 |
| 0.6839 | 3.2 | 1400 | 0.7754 | 0.6743 | 0.6770 |
| 0.6699 | 3.66 | 1600 | 0.7853 | 0.6711 | 0.6666 |
| 0.6502 | 4.11 | 1800 | 0.7789 | 0.671 | 0.6692 |
| 0.6431 | 4.57 | 2000 | 0.7864 | 0.6741 | 0.6719 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|