|
--- |
|
license: mit |
|
base_model: microsoft/mdeberta-v3-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- massive |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: scenario-TCR_data-en-massive_all_1_1 |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: massive |
|
type: massive |
|
config: all_1.1 |
|
split: validation |
|
args: all_1.1 |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.7256830917315278 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6761346748529903 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# scenario-TCR_data-en-massive_all_1_1 |
|
|
|
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.6335 |
|
- Accuracy: 0.7257 |
|
- F1: 0.6761 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 66 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
|
| No log | 0.28 | 100 | 2.9382 | 0.2614 | 0.0710 | |
|
| No log | 0.56 | 200 | 1.9636 | 0.5368 | 0.2848 | |
|
| No log | 0.83 | 300 | 1.7094 | 0.5934 | 0.3887 | |
|
| No log | 1.11 | 400 | 1.5733 | 0.6305 | 0.4633 | |
|
| 1.8822 | 1.39 | 500 | 1.4046 | 0.6635 | 0.5200 | |
|
| 1.8822 | 1.67 | 600 | 1.4016 | 0.6794 | 0.5558 | |
|
| 1.8822 | 1.94 | 700 | 1.4019 | 0.6775 | 0.5858 | |
|
| 1.8822 | 2.22 | 800 | 1.3179 | 0.7026 | 0.6044 | |
|
| 1.8822 | 2.5 | 900 | 1.3087 | 0.7145 | 0.6295 | |
|
| 0.576 | 2.78 | 1000 | 1.4452 | 0.6947 | 0.6119 | |
|
| 0.576 | 3.06 | 1100 | 1.5017 | 0.6958 | 0.6297 | |
|
| 0.576 | 3.33 | 1200 | 1.3701 | 0.7107 | 0.6439 | |
|
| 0.576 | 3.61 | 1300 | 1.4868 | 0.7064 | 0.6435 | |
|
| 0.576 | 3.89 | 1400 | 1.3839 | 0.7175 | 0.6397 | |
|
| 0.3185 | 4.17 | 1500 | 1.5691 | 0.7013 | 0.6411 | |
|
| 0.3185 | 4.44 | 1600 | 1.5106 | 0.7084 | 0.6481 | |
|
| 0.3185 | 4.72 | 1700 | 1.6129 | 0.6979 | 0.6499 | |
|
| 0.3185 | 5.0 | 1800 | 1.5121 | 0.7142 | 0.6551 | |
|
| 0.3185 | 5.28 | 1900 | 1.6968 | 0.7039 | 0.6432 | |
|
| 0.1966 | 5.56 | 2000 | 1.7057 | 0.7012 | 0.6333 | |
|
| 0.1966 | 5.83 | 2100 | 1.6411 | 0.7165 | 0.6564 | |
|
| 0.1966 | 6.11 | 2200 | 1.5510 | 0.7274 | 0.6709 | |
|
| 0.1966 | 6.39 | 2300 | 1.7691 | 0.7172 | 0.6623 | |
|
| 0.1966 | 6.67 | 2400 | 1.7955 | 0.7152 | 0.6529 | |
|
| 0.156 | 6.94 | 2500 | 1.9122 | 0.7018 | 0.6548 | |
|
| 0.156 | 7.22 | 2600 | 1.7143 | 0.7242 | 0.6694 | |
|
| 0.156 | 7.5 | 2700 | 1.9184 | 0.7071 | 0.6528 | |
|
| 0.156 | 7.78 | 2800 | 1.9581 | 0.7086 | 0.6454 | |
|
| 0.156 | 8.06 | 2900 | 1.7750 | 0.7203 | 0.6643 | |
|
| 0.0983 | 8.33 | 3000 | 1.9790 | 0.7136 | 0.6658 | |
|
| 0.0983 | 8.61 | 3100 | 1.9127 | 0.7101 | 0.6499 | |
|
| 0.0983 | 8.89 | 3200 | 2.0017 | 0.7121 | 0.6501 | |
|
| 0.0983 | 9.17 | 3300 | 1.9420 | 0.7216 | 0.6650 | |
|
| 0.0983 | 9.44 | 3400 | 2.0679 | 0.7082 | 0.6517 | |
|
| 0.0767 | 9.72 | 3500 | 2.1093 | 0.7046 | 0.6458 | |
|
| 0.0767 | 10.0 | 3600 | 2.1402 | 0.7126 | 0.6600 | |
|
| 0.0767 | 10.28 | 3700 | 2.0547 | 0.7157 | 0.6578 | |
|
| 0.0767 | 10.56 | 3800 | 2.1029 | 0.7180 | 0.6624 | |
|
| 0.0767 | 10.83 | 3900 | 2.2774 | 0.7075 | 0.6501 | |
|
| 0.0532 | 11.11 | 4000 | 2.2711 | 0.7005 | 0.6460 | |
|
| 0.0532 | 11.39 | 4100 | 2.2347 | 0.7038 | 0.6500 | |
|
| 0.0532 | 11.67 | 4200 | 2.3489 | 0.6997 | 0.6462 | |
|
| 0.0532 | 11.94 | 4300 | 2.3262 | 0.7092 | 0.6539 | |
|
| 0.0532 | 12.22 | 4400 | 2.4171 | 0.6990 | 0.6523 | |
|
| 0.0378 | 12.5 | 4500 | 2.2400 | 0.7145 | 0.6600 | |
|
| 0.0378 | 12.78 | 4600 | 2.2622 | 0.7107 | 0.6518 | |
|
| 0.0378 | 13.06 | 4700 | 2.2886 | 0.6952 | 0.6397 | |
|
| 0.0378 | 13.33 | 4800 | 2.2268 | 0.7128 | 0.6570 | |
|
| 0.0378 | 13.61 | 4900 | 2.3858 | 0.7022 | 0.6453 | |
|
| 0.0307 | 13.89 | 5000 | 2.2298 | 0.7171 | 0.6609 | |
|
| 0.0307 | 14.17 | 5100 | 2.3298 | 0.7183 | 0.6599 | |
|
| 0.0307 | 14.44 | 5200 | 2.3642 | 0.7117 | 0.6502 | |
|
| 0.0307 | 14.72 | 5300 | 2.4279 | 0.7179 | 0.6681 | |
|
| 0.0307 | 15.0 | 5400 | 2.5524 | 0.6995 | 0.6481 | |
|
| 0.0264 | 15.28 | 5500 | 2.4293 | 0.7121 | 0.6596 | |
|
| 0.0264 | 15.56 | 5600 | 2.3810 | 0.7163 | 0.6583 | |
|
| 0.0264 | 15.83 | 5700 | 2.2901 | 0.7317 | 0.6745 | |
|
| 0.0264 | 16.11 | 5800 | 2.3646 | 0.7250 | 0.6696 | |
|
| 0.0264 | 16.39 | 5900 | 2.3795 | 0.7233 | 0.6718 | |
|
| 0.019 | 16.67 | 6000 | 2.5199 | 0.7153 | 0.6647 | |
|
| 0.019 | 16.94 | 6100 | 2.4350 | 0.7222 | 0.6719 | |
|
| 0.019 | 17.22 | 6200 | 2.4837 | 0.7180 | 0.6702 | |
|
| 0.019 | 17.5 | 6300 | 2.4684 | 0.7230 | 0.6756 | |
|
| 0.019 | 17.78 | 6400 | 2.4124 | 0.7241 | 0.6743 | |
|
| 0.0144 | 18.06 | 6500 | 2.5430 | 0.7170 | 0.6709 | |
|
| 0.0144 | 18.33 | 6600 | 2.5298 | 0.7104 | 0.6599 | |
|
| 0.0144 | 18.61 | 6700 | 2.4784 | 0.7217 | 0.6716 | |
|
| 0.0144 | 18.89 | 6800 | 2.5899 | 0.7101 | 0.6703 | |
|
| 0.0144 | 19.17 | 6900 | 2.4036 | 0.7317 | 0.6815 | |
|
| 0.0127 | 19.44 | 7000 | 2.5389 | 0.7188 | 0.6696 | |
|
| 0.0127 | 19.72 | 7100 | 2.4397 | 0.7263 | 0.6767 | |
|
| 0.0127 | 20.0 | 7200 | 2.3838 | 0.7264 | 0.6734 | |
|
| 0.0127 | 20.28 | 7300 | 2.4933 | 0.7222 | 0.6763 | |
|
| 0.0127 | 20.56 | 7400 | 2.4831 | 0.7291 | 0.6773 | |
|
| 0.0077 | 20.83 | 7500 | 2.4833 | 0.7255 | 0.6747 | |
|
| 0.0077 | 21.11 | 7600 | 2.5969 | 0.7188 | 0.6728 | |
|
| 0.0077 | 21.39 | 7700 | 2.5866 | 0.7180 | 0.6739 | |
|
| 0.0077 | 21.67 | 7800 | 2.5581 | 0.7255 | 0.6799 | |
|
| 0.0077 | 21.94 | 7900 | 2.5420 | 0.7266 | 0.6764 | |
|
| 0.0052 | 22.22 | 8000 | 2.6534 | 0.7184 | 0.6670 | |
|
| 0.0052 | 22.5 | 8100 | 2.5060 | 0.7286 | 0.6797 | |
|
| 0.0052 | 22.78 | 8200 | 2.5219 | 0.7283 | 0.6823 | |
|
| 0.0052 | 23.06 | 8300 | 2.5787 | 0.7220 | 0.6804 | |
|
| 0.0052 | 23.33 | 8400 | 2.6081 | 0.7228 | 0.6784 | |
|
| 0.0047 | 23.61 | 8500 | 2.5537 | 0.7271 | 0.6786 | |
|
| 0.0047 | 23.89 | 8600 | 2.6520 | 0.7229 | 0.6776 | |
|
| 0.0047 | 24.17 | 8700 | 2.6277 | 0.7261 | 0.6791 | |
|
| 0.0047 | 24.44 | 8800 | 2.6475 | 0.7231 | 0.6759 | |
|
| 0.0047 | 24.72 | 8900 | 2.6349 | 0.7232 | 0.6754 | |
|
| 0.0031 | 25.0 | 9000 | 2.5821 | 0.7256 | 0.6747 | |
|
| 0.0031 | 25.28 | 9100 | 2.6122 | 0.7241 | 0.6744 | |
|
| 0.0031 | 25.56 | 9200 | 2.6335 | 0.7223 | 0.6727 | |
|
| 0.0031 | 25.83 | 9300 | 2.6440 | 0.7237 | 0.6736 | |
|
| 0.0031 | 26.11 | 9400 | 2.6027 | 0.7257 | 0.6746 | |
|
| 0.0017 | 26.39 | 9500 | 2.6251 | 0.7240 | 0.6735 | |
|
| 0.0017 | 26.67 | 9600 | 2.7213 | 0.7177 | 0.6711 | |
|
| 0.0017 | 26.94 | 9700 | 2.7145 | 0.7190 | 0.6712 | |
|
| 0.0017 | 27.22 | 9800 | 2.6901 | 0.7208 | 0.6722 | |
|
| 0.0017 | 27.5 | 9900 | 2.6853 | 0.7207 | 0.6724 | |
|
| 0.0015 | 27.78 | 10000 | 2.6557 | 0.7223 | 0.6731 | |
|
| 0.0015 | 28.06 | 10100 | 2.6671 | 0.7224 | 0.6728 | |
|
| 0.0015 | 28.33 | 10200 | 2.6418 | 0.7236 | 0.6744 | |
|
| 0.0015 | 28.61 | 10300 | 2.6298 | 0.7255 | 0.6755 | |
|
| 0.0015 | 28.89 | 10400 | 2.6226 | 0.7265 | 0.6775 | |
|
| 0.0008 | 29.17 | 10500 | 2.6252 | 0.7267 | 0.6773 | |
|
| 0.0008 | 29.44 | 10600 | 2.6322 | 0.7262 | 0.6766 | |
|
| 0.0008 | 29.72 | 10700 | 2.6345 | 0.7255 | 0.6761 | |
|
| 0.0008 | 30.0 | 10800 | 2.6335 | 0.7257 | 0.6761 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
