|
--- |
|
license: mit |
|
base_model: facebook/xlm-v-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: 0.7100778333960244 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6550778448597152 |
|
--- |
|
|
|
<!-- 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 [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.3802 |
|
- Accuracy: 0.7101 |
|
- F1: 0.6551 |
|
|
|
## 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: 42 |
|
- 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 | 3.6542 | 0.0800 | 0.0085 | |
|
| No log | 0.56 | 200 | 2.9766 | 0.3048 | 0.0953 | |
|
| No log | 0.83 | 300 | 2.4835 | 0.3498 | 0.1168 | |
|
| No log | 1.11 | 400 | 2.1305 | 0.4616 | 0.2154 | |
|
| 2.7657 | 1.39 | 500 | 1.8889 | 0.5374 | 0.2791 | |
|
| 2.7657 | 1.67 | 600 | 1.7326 | 0.5726 | 0.3208 | |
|
| 2.7657 | 1.94 | 700 | 1.6536 | 0.5870 | 0.3726 | |
|
| 2.7657 | 2.22 | 800 | 1.6709 | 0.5987 | 0.4014 | |
|
| 2.7657 | 2.5 | 900 | 1.5460 | 0.6337 | 0.4720 | |
|
| 1.1591 | 2.78 | 1000 | 1.5165 | 0.6434 | 0.4904 | |
|
| 1.1591 | 3.06 | 1100 | 1.3861 | 0.6736 | 0.5237 | |
|
| 1.1591 | 3.33 | 1200 | 1.3776 | 0.6739 | 0.5320 | |
|
| 1.1591 | 3.61 | 1300 | 1.3753 | 0.6734 | 0.5521 | |
|
| 1.1591 | 3.89 | 1400 | 1.4680 | 0.6624 | 0.5368 | |
|
| 0.6194 | 4.17 | 1500 | 1.3899 | 0.6795 | 0.5520 | |
|
| 0.6194 | 4.44 | 1600 | 1.5509 | 0.6640 | 0.5482 | |
|
| 0.6194 | 4.72 | 1700 | 1.4034 | 0.6837 | 0.5764 | |
|
| 0.6194 | 5.0 | 1800 | 1.4750 | 0.6739 | 0.5814 | |
|
| 0.6194 | 5.28 | 1900 | 1.5321 | 0.6697 | 0.5761 | |
|
| 0.3858 | 5.56 | 2000 | 1.5022 | 0.6822 | 0.5912 | |
|
| 0.3858 | 5.83 | 2100 | 1.4612 | 0.6865 | 0.6016 | |
|
| 0.3858 | 6.11 | 2200 | 1.4079 | 0.7034 | 0.6204 | |
|
| 0.3858 | 6.39 | 2300 | 1.5165 | 0.6922 | 0.6296 | |
|
| 0.3858 | 6.67 | 2400 | 1.6168 | 0.6736 | 0.6157 | |
|
| 0.259 | 6.94 | 2500 | 1.5425 | 0.6948 | 0.6261 | |
|
| 0.259 | 7.22 | 2600 | 1.6145 | 0.6796 | 0.6035 | |
|
| 0.259 | 7.5 | 2700 | 1.5916 | 0.6824 | 0.6175 | |
|
| 0.259 | 7.78 | 2800 | 1.5966 | 0.6977 | 0.6306 | |
|
| 0.259 | 8.06 | 2900 | 1.4939 | 0.7125 | 0.6274 | |
|
| 0.1759 | 8.33 | 3000 | 1.8425 | 0.6714 | 0.6170 | |
|
| 0.1759 | 8.61 | 3100 | 1.6688 | 0.6923 | 0.6403 | |
|
| 0.1759 | 8.89 | 3200 | 1.6218 | 0.6997 | 0.6220 | |
|
| 0.1759 | 9.17 | 3300 | 1.7825 | 0.6829 | 0.6223 | |
|
| 0.1759 | 9.44 | 3400 | 1.8706 | 0.6916 | 0.6294 | |
|
| 0.1162 | 9.72 | 3500 | 1.8082 | 0.6884 | 0.6280 | |
|
| 0.1162 | 10.0 | 3600 | 1.6708 | 0.7096 | 0.6338 | |
|
| 0.1162 | 10.28 | 3700 | 1.7170 | 0.7100 | 0.6490 | |
|
| 0.1162 | 10.56 | 3800 | 1.8575 | 0.6917 | 0.6264 | |
|
| 0.1162 | 10.83 | 3900 | 1.8307 | 0.6959 | 0.6448 | |
|
| 0.092 | 11.11 | 4000 | 1.9248 | 0.6958 | 0.6359 | |
|
| 0.092 | 11.39 | 4100 | 1.7551 | 0.7162 | 0.6508 | |
|
| 0.092 | 11.67 | 4200 | 1.8234 | 0.7072 | 0.6465 | |
|
| 0.092 | 11.94 | 4300 | 2.1146 | 0.6790 | 0.6285 | |
|
| 0.092 | 12.22 | 4400 | 1.9964 | 0.6909 | 0.6411 | |
|
| 0.0582 | 12.5 | 4500 | 2.0290 | 0.6852 | 0.6313 | |
|
| 0.0582 | 12.78 | 4600 | 2.0828 | 0.6838 | 0.6355 | |
|
| 0.0582 | 13.06 | 4700 | 1.9272 | 0.7013 | 0.6312 | |
|
| 0.0582 | 13.33 | 4800 | 1.9882 | 0.6959 | 0.6334 | |
|
| 0.0582 | 13.61 | 4900 | 1.9552 | 0.7116 | 0.6511 | |
|
| 0.0398 | 13.89 | 5000 | 2.0269 | 0.7060 | 0.6451 | |
|
| 0.0398 | 14.17 | 5100 | 2.1377 | 0.6929 | 0.6414 | |
|
| 0.0398 | 14.44 | 5200 | 2.1114 | 0.6880 | 0.6373 | |
|
| 0.0398 | 14.72 | 5300 | 2.1517 | 0.6927 | 0.6438 | |
|
| 0.0398 | 15.0 | 5400 | 2.2472 | 0.6921 | 0.6499 | |
|
| 0.0311 | 15.28 | 5500 | 2.1801 | 0.6993 | 0.6557 | |
|
| 0.0311 | 15.56 | 5600 | 2.1090 | 0.7020 | 0.6458 | |
|
| 0.0311 | 15.83 | 5700 | 2.0049 | 0.7160 | 0.6590 | |
|
| 0.0311 | 16.11 | 5800 | 2.2198 | 0.6959 | 0.6460 | |
|
| 0.0311 | 16.39 | 5900 | 2.1074 | 0.7087 | 0.6519 | |
|
| 0.0223 | 16.67 | 6000 | 2.0899 | 0.7096 | 0.6563 | |
|
| 0.0223 | 16.94 | 6100 | 2.1736 | 0.7026 | 0.6546 | |
|
| 0.0223 | 17.22 | 6200 | 2.1829 | 0.7004 | 0.6496 | |
|
| 0.0223 | 17.5 | 6300 | 2.2041 | 0.6973 | 0.6450 | |
|
| 0.0223 | 17.78 | 6400 | 2.1969 | 0.7074 | 0.6566 | |
|
| 0.0178 | 18.06 | 6500 | 2.4021 | 0.6931 | 0.6515 | |
|
| 0.0178 | 18.33 | 6600 | 2.2865 | 0.7092 | 0.6619 | |
|
| 0.0178 | 18.61 | 6700 | 2.3086 | 0.7018 | 0.6504 | |
|
| 0.0178 | 18.89 | 6800 | 2.2665 | 0.7054 | 0.6535 | |
|
| 0.0178 | 19.17 | 6900 | 2.2723 | 0.7061 | 0.6525 | |
|
| 0.0129 | 19.44 | 7000 | 2.2976 | 0.7030 | 0.6483 | |
|
| 0.0129 | 19.72 | 7100 | 2.3634 | 0.7011 | 0.6514 | |
|
| 0.0129 | 20.0 | 7200 | 2.3313 | 0.6971 | 0.6464 | |
|
| 0.0129 | 20.28 | 7300 | 2.4373 | 0.6907 | 0.6439 | |
|
| 0.0129 | 20.56 | 7400 | 2.2424 | 0.7139 | 0.6588 | |
|
| 0.0125 | 20.83 | 7500 | 2.2329 | 0.7098 | 0.6547 | |
|
| 0.0125 | 21.11 | 7600 | 2.2365 | 0.7107 | 0.6607 | |
|
| 0.0125 | 21.39 | 7700 | 2.2925 | 0.7096 | 0.6593 | |
|
| 0.0125 | 21.67 | 7800 | 2.3717 | 0.6998 | 0.6486 | |
|
| 0.0125 | 21.94 | 7900 | 2.4211 | 0.6951 | 0.6479 | |
|
| 0.0104 | 22.22 | 8000 | 2.3714 | 0.6978 | 0.6434 | |
|
| 0.0104 | 22.5 | 8100 | 2.3995 | 0.7004 | 0.6503 | |
|
| 0.0104 | 22.78 | 8200 | 2.3877 | 0.7044 | 0.6521 | |
|
| 0.0104 | 23.06 | 8300 | 2.4957 | 0.6972 | 0.6482 | |
|
| 0.0104 | 23.33 | 8400 | 2.2553 | 0.7180 | 0.6591 | |
|
| 0.0061 | 23.61 | 8500 | 2.3877 | 0.7068 | 0.6560 | |
|
| 0.0061 | 23.89 | 8600 | 2.4298 | 0.7036 | 0.6557 | |
|
| 0.0061 | 24.17 | 8700 | 2.3903 | 0.7055 | 0.6516 | |
|
| 0.0061 | 24.44 | 8800 | 2.3298 | 0.7065 | 0.6493 | |
|
| 0.0061 | 24.72 | 8900 | 2.3245 | 0.7110 | 0.6535 | |
|
| 0.0054 | 25.0 | 9000 | 2.3287 | 0.7086 | 0.6494 | |
|
| 0.0054 | 25.28 | 9100 | 2.4519 | 0.6989 | 0.6427 | |
|
| 0.0054 | 25.56 | 9200 | 2.4671 | 0.6988 | 0.6421 | |
|
| 0.0054 | 25.83 | 9300 | 2.5166 | 0.6955 | 0.6447 | |
|
| 0.0054 | 26.11 | 9400 | 2.4190 | 0.7056 | 0.6500 | |
|
| 0.0029 | 26.39 | 9500 | 2.4361 | 0.7049 | 0.6511 | |
|
| 0.0029 | 26.67 | 9600 | 2.4765 | 0.7029 | 0.6496 | |
|
| 0.0029 | 26.94 | 9700 | 2.5246 | 0.6988 | 0.6460 | |
|
| 0.0029 | 27.22 | 9800 | 2.4363 | 0.7051 | 0.6491 | |
|
| 0.0029 | 27.5 | 9900 | 2.4066 | 0.7075 | 0.6514 | |
|
| 0.0025 | 27.78 | 10000 | 2.3870 | 0.7092 | 0.6556 | |
|
| 0.0025 | 28.06 | 10100 | 2.4028 | 0.7081 | 0.6539 | |
|
| 0.0025 | 28.33 | 10200 | 2.3983 | 0.7080 | 0.6537 | |
|
| 0.0025 | 28.61 | 10300 | 2.3876 | 0.7088 | 0.6552 | |
|
| 0.0025 | 28.89 | 10400 | 2.4032 | 0.7080 | 0.6542 | |
|
| 0.0025 | 29.17 | 10500 | 2.4138 | 0.7081 | 0.6544 | |
|
| 0.0025 | 29.44 | 10600 | 2.3880 | 0.7098 | 0.6555 | |
|
| 0.0025 | 29.72 | 10700 | 2.3801 | 0.7100 | 0.6552 | |
|
| 0.0025 | 30.0 | 10800 | 2.3802 | 0.7101 | 0.6551 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
