File size: 2,425 Bytes
b2a8170 |
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 |
---
language:
- en
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: mdeberta-v3-base-sst2-10
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tmnam20/VieGLUE/SST2
type: tmnam20/VieGLUE
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8979357798165137
---
<!-- 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. -->
# mdeberta-v3-base-sst2-10
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3852
- Accuracy: 0.8979
## 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: 32
- eval_batch_size: 16
- seed: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3449 | 0.24 | 500 | 0.3368 | 0.8567 |
| 0.2987 | 0.48 | 1000 | 0.3037 | 0.8716 |
| 0.2492 | 0.71 | 1500 | 0.3347 | 0.8842 |
| 0.24 | 0.95 | 2000 | 0.2953 | 0.8830 |
| 0.195 | 1.19 | 2500 | 0.3445 | 0.8842 |
| 0.1934 | 1.43 | 3000 | 0.3217 | 0.8876 |
| 0.1697 | 1.66 | 3500 | 0.3627 | 0.8876 |
| 0.1757 | 1.9 | 4000 | 0.3366 | 0.8899 |
| 0.1328 | 2.14 | 4500 | 0.4266 | 0.8876 |
| 0.1475 | 2.38 | 5000 | 0.3737 | 0.8933 |
| 0.1574 | 2.61 | 5500 | 0.3888 | 0.8911 |
| 0.1548 | 2.85 | 6000 | 0.4063 | 0.8865 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|