File size: 3,690 Bytes
7dc678a c94f474 7dc678a c613ada 7dc678a c613ada 84977da 7dc678a c94f474 215c743 84977da 215c743 7dc678a be49bb1 7dc678a be49bb1 7dc678a c613ada 84977da 7dc678a c613ada 7dc678a |
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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
base_model: huawei-noah/TinyBERT_General_4L_312D
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
model-index:
- name: TinyBERT_SST2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sst2
type: sst2
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8864678899082569
---
<!-- 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. -->
# TinyBERT_SST2
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5142
- Accuracy: 0.8865
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4686 | 0.06 | 500 | 0.4020 | 0.8337 |
| 0.384 | 0.12 | 1000 | 0.3666 | 0.8360 |
| 0.381 | 0.18 | 1500 | 0.3951 | 0.8337 |
| 0.3609 | 0.24 | 2000 | 0.4378 | 0.8555 |
| 0.3616 | 0.3 | 2500 | 0.3743 | 0.8475 |
| 0.3521 | 0.36 | 3000 | 0.3692 | 0.8589 |
| 0.3113 | 0.42 | 3500 | 0.5072 | 0.8486 |
| 0.319 | 0.48 | 4000 | 0.4212 | 0.8612 |
| 0.3034 | 0.53 | 4500 | 0.4555 | 0.8647 |
| 0.3098 | 0.59 | 5000 | 0.4163 | 0.8635 |
| 0.3113 | 0.65 | 5500 | 0.5226 | 0.8440 |
| 0.2949 | 0.71 | 6000 | 0.4137 | 0.875 |
| 0.2977 | 0.77 | 6500 | 0.4775 | 0.8486 |
| 0.3077 | 0.83 | 7000 | 0.4774 | 0.8693 |
| 0.2953 | 0.89 | 7500 | 0.4491 | 0.8589 |
| 0.2846 | 0.95 | 8000 | 0.5228 | 0.8784 |
| 0.292 | 1.01 | 8500 | 0.4801 | 0.8865 |
| 0.2185 | 1.07 | 9000 | 0.4889 | 0.8933 |
| 0.2343 | 1.13 | 9500 | 0.5862 | 0.8716 |
| 0.2667 | 1.19 | 10000 | 0.4796 | 0.8842 |
| 0.252 | 1.25 | 10500 | 0.5181 | 0.8842 |
| 0.2385 | 1.31 | 11000 | 0.5148 | 0.875 |
| 0.2144 | 1.37 | 11500 | 0.5345 | 0.8704 |
| 0.2348 | 1.43 | 12000 | 0.5073 | 0.8807 |
| 0.2166 | 1.48 | 12500 | 0.4885 | 0.8865 |
| 0.2104 | 1.54 | 13000 | 0.6118 | 0.8658 |
| 0.2145 | 1.6 | 13500 | 0.5091 | 0.8865 |
| 0.2098 | 1.66 | 14000 | 0.5221 | 0.8876 |
| 0.2111 | 1.72 | 14500 | 0.5031 | 0.8888 |
| 0.2042 | 1.78 | 15000 | 0.5257 | 0.8796 |
| 0.2091 | 1.84 | 15500 | 0.5175 | 0.8819 |
| 0.2027 | 1.9 | 16000 | 0.5528 | 0.8784 |
| 0.2173 | 1.96 | 16500 | 0.5142 | 0.8865 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|