Action_agent
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the agent_action_class dataset. It achieves the following results on the evaluation set:
Loss: 0.9962
Accuracy: 0.8243
Confusion Matrix: [[39, 3, 0, 0, 2, 1, 0, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 2, 1, 4, 0, 5, 0, 0], [4, 1, 0, 39, 0, 3, 0, 0, 0, 8], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 1, 44, 1, 0, 0, 2], [3, 0, 0, 1, 1, 0, 55, 0, 2, 1], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [2, 9, 0, 0, 0, 0, 9, 1, 39, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]]
Classification Report: precision recall f1-score support
0 0.7800 0.7500 0.7647 52 1 0.8028 0.9500 0.8702 60 2 0.7600 0.7451 0.7525 51 3 0.8298 0.7091 0.7647 55 4 0.9091 0.8929 0.9009 56 5 0.8302 0.7857 0.8073 56 6 0.8333 0.8730 0.8527 63 7 0.8667 0.9286 0.8966 56 8 0.8667 0.6500 0.7429 60 9 0.7778 0.9333 0.8485 60accuracy 0.8243 569 macro avg 0.8256 0.8218 0.8201 569
weighted avg 0.8264 0.8243 0.8216 569
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
|---|---|---|---|---|---|---|
| 2.1982 | 0.75 | 100 | 2.1583 | 0.4851 | [[2, 3, 2, 1, 3, 1, 7, 15, 10, 8], [1, 52, 0, 0, 2, 0, 0, 2, 2, 1], [1, 0, 15, 0, 5, 0, 3, 23, 3, 1], [2, 1, 8, 12, 5, 0, 6, 6, 1, 14], [0, 2, 9, 1, 30, 2, 2, 3, 2, 5], [0, 2, 6, 2, 5, 16, 2, 16, 4, 3], [0, 7, 0, 1, 5, 2, 27, 1, 12, 8], [0, 0, 1, 0, 0, 0, 1, 54, 0, 0], [0, 11, 1, 0, 3, 2, 5, 7, 31, 0], [0, 3, 4, 1, 4, 1, 1, 6, 3, 37]] | precision recall f1-score support |
0 0.3333 0.0385 0.0690 52
1 0.6420 0.8667 0.7376 60
2 0.3261 0.2941 0.3093 51
3 0.6667 0.2182 0.3288 55
4 0.4839 0.5357 0.5085 56
5 0.6667 0.2857 0.4000 56
6 0.5000 0.4286 0.4615 63
7 0.4060 0.9643 0.5714 56
8 0.4559 0.5167 0.4844 60
9 0.4805 0.6167 0.5401 60
accuracy 0.4851 569
macro avg 0.4961 0.4765 0.4411 569 weighted avg 0.4991 0.4851 0.4484 569 | | 1.988 | 1.49 | 200 | 1.9350 | 0.6257 | [[11, 6, 2, 0, 7, 1, 3, 10, 7, 5], [0, 58, 0, 0, 1, 0, 0, 0, 1, 0], [1, 1, 19, 0, 4, 1, 1, 24, 0, 0], [1, 1, 5, 16, 3, 0, 6, 7, 0, 16], [1, 1, 1, 0, 50, 0, 2, 0, 0, 1], [1, 0, 11, 0, 6, 25, 0, 11, 0, 2], [2, 8, 1, 1, 3, 1, 38, 2, 5, 2], [0, 0, 1, 0, 0, 0, 0, 55, 0, 0], [1, 12, 0, 0, 1, 1, 5, 6, 34, 0], [1, 0, 2, 3, 2, 0, 0, 2, 0, 50]] | precision recall f1-score support
0 0.5789 0.2115 0.3099 52
1 0.6667 0.9667 0.7891 60
2 0.4524 0.3725 0.4086 51
3 0.8000 0.2909 0.4267 55
4 0.6494 0.8929 0.7519 56
5 0.8621 0.4464 0.5882 56
6 0.6909 0.6032 0.6441 63
7 0.4701 0.9821 0.6358 56
8 0.7234 0.5667 0.6355 60
9 0.6579 0.8333 0.7353 60
accuracy 0.6257 569
macro avg 0.6552 0.6166 0.5925 569 weighted avg 0.6583 0.6257 0.5997 569 | | 1.7347 | 2.24 | 300 | 1.6937 | 0.7223 | [[28, 4, 2, 1, 4, 1, 1, 1, 6, 4], [0, 58, 0, 0, 0, 0, 1, 0, 1, 0], [3, 0, 28, 0, 1, 1, 1, 16, 0, 1], [2, 2, 2, 29, 1, 0, 2, 2, 0, 15], [2, 1, 1, 0, 49, 0, 1, 0, 0, 2], [1, 0, 6, 0, 3, 35, 1, 8, 0, 2], [4, 5, 1, 1, 1, 0, 38, 1, 10, 2], [0, 0, 0, 0, 0, 0, 0, 56, 0, 0], [6, 11, 0, 0, 1, 0, 5, 2, 35, 0], [0, 0, 2, 2, 0, 0, 0, 1, 0, 55]] | precision recall f1-score support
0 0.6087 0.5385 0.5714 52
1 0.7160 0.9667 0.8227 60
2 0.6667 0.5490 0.6022 51
3 0.8788 0.5273 0.6591 55
4 0.8167 0.8750 0.8448 56
5 0.9459 0.6250 0.7527 56
6 0.7600 0.6032 0.6726 63
7 0.6437 1.0000 0.7832 56
8 0.6731 0.5833 0.6250 60
9 0.6790 0.9167 0.7801 60
accuracy 0.7223 569
macro avg 0.7389 0.7185 0.7114 569 weighted avg 0.7394 0.7223 0.7136 569 | | 1.5713 | 2.99 | 400 | 1.4857 | 0.7434 | [[26, 6, 2, 1, 5, 1, 0, 2, 5, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 29, 1, 2, 2, 2, 13, 0, 0], [3, 1, 4, 32, 1, 1, 0, 1, 0, 12], [1, 1, 1, 0, 49, 0, 1, 0, 0, 3], [1, 0, 6, 0, 4, 41, 0, 2, 0, 2], [3, 5, 1, 0, 1, 0, 42, 0, 8, 3], [0, 0, 0, 1, 0, 0, 0, 55, 0, 0], [4, 11, 0, 0, 0, 0, 8, 2, 35, 0], [0, 0, 2, 0, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
0 0.6500 0.5000 0.5652 52
1 0.7037 0.9500 0.8085 60
2 0.6444 0.5686 0.6042 51
3 0.9143 0.5818 0.7111 55
4 0.7903 0.8750 0.8305 56
5 0.9111 0.7321 0.8119 56
6 0.7778 0.6667 0.7179 63
7 0.7237 0.9821 0.8333 56
8 0.7143 0.5833 0.6422 60
9 0.6951 0.9500 0.8028 60
accuracy 0.7434 569
macro avg 0.7525 0.7390 0.7328 569 weighted avg 0.7532 0.7434 0.7353 569 | | 1.3821 | 3.73 | 500 | 1.3477 | 0.7575 | [[30, 4, 0, 3, 4, 1, 0, 2, 4, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 30, 4, 1, 2, 1, 10, 0, 1], [3, 2, 2, 27, 0, 1, 0, 2, 0, 18], [1, 1, 1, 0, 49, 0, 1, 0, 0, 3], [1, 0, 5, 0, 1, 44, 1, 1, 0, 3], [4, 0, 1, 1, 1, 0, 49, 0, 3, 4], [0, 0, 2, 1, 0, 0, 0, 53, 0, 0], [3, 11, 0, 0, 0, 0, 10, 2, 34, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 58]] | precision recall f1-score support
0 0.6818 0.5769 0.6250 52
1 0.7600 0.9500 0.8444 60
2 0.7143 0.5882 0.6452 51
3 0.7500 0.4909 0.5934 55
4 0.8750 0.8750 0.8750 56
5 0.9167 0.7857 0.8462 56
6 0.7778 0.7778 0.7778 63
7 0.7465 0.9464 0.8346 56
8 0.8095 0.5667 0.6667 60
9 0.6304 0.9667 0.7632 60
accuracy 0.7575 569
macro avg 0.7662 0.7524 0.7471 569 weighted avg 0.7667 0.7575 0.7498 569 | | 1.3065 | 4.48 | 600 | 1.2437 | 0.7856 | [[33, 4, 0, 1, 3, 1, 0, 2, 4, 4], [0, 56, 0, 0, 0, 0, 1, 0, 2, 1], [1, 0, 29, 5, 1, 2, 1, 12, 0, 0], [2, 1, 1, 36, 0, 3, 0, 2, 0, 10], [1, 1, 1, 1, 50, 0, 0, 0, 0, 2], [1, 0, 4, 1, 1, 42, 1, 4, 0, 2], [3, 0, 0, 0, 1, 0, 53, 0, 3, 3], [0, 0, 0, 1, 0, 0, 0, 55, 0, 0], [4, 9, 0, 0, 0, 0, 9, 1, 37, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7333 0.6346 0.6804 52
1 0.7887 0.9333 0.8550 60
2 0.8286 0.5686 0.6744 51
3 0.7660 0.6545 0.7059 55
4 0.8929 0.8929 0.8929 56
5 0.8571 0.7500 0.8000 56
6 0.8154 0.8413 0.8281 63
7 0.7143 0.9821 0.8271 56
8 0.8043 0.6167 0.6981 60
9 0.7179 0.9333 0.8116 60
accuracy 0.7856 569
macro avg 0.7919 0.7807 0.7773 569 weighted avg 0.7918 0.7856 0.7799 569 | | 1.2329 | 5.22 | 700 | 1.1645 | 0.7909 | [[34, 4, 0, 1, 3, 1, 0, 1, 4, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 33, 5, 1, 3, 1, 7, 0, 0], [3, 1, 1, 31, 1, 2, 0, 1, 0, 15], [1, 1, 1, 1, 50, 0, 0, 0, 0, 2], [1, 0, 7, 1, 2, 43, 0, 0, 0, 2], [2, 0, 0, 0, 1, 0, 56, 0, 1, 3], [0, 0, 2, 1, 0, 0, 0, 53, 0, 0], [2, 11, 0, 0, 0, 0, 10, 1, 36, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 57]] | precision recall f1-score support
0 0.7727 0.6538 0.7083 52
1 0.7703 0.9500 0.8507 60
2 0.7500 0.6471 0.6947 51
3 0.7561 0.5636 0.6458 55
4 0.8621 0.8929 0.8772 56
5 0.8600 0.7679 0.8113 56
6 0.8235 0.8889 0.8550 63
7 0.8281 0.9464 0.8833 56
8 0.8571 0.6000 0.7059 60
9 0.6786 0.9500 0.7917 60
accuracy 0.7909 569
macro avg 0.7959 0.7861 0.7824 569 weighted avg 0.7963 0.7909 0.7848 569 | | 1.1736 | 5.97 | 800 | 1.1159 | 0.7891 | [[35, 4, 0, 0, 2, 1, 1, 1, 4, 4], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 35, 2, 1, 3, 1, 7, 0, 0], [3, 1, 0, 34, 0, 3, 0, 1, 0, 13], [1, 1, 2, 1, 49, 0, 0, 0, 0, 2], [1, 0, 7, 1, 1, 43, 1, 0, 0, 2], [3, 0, 0, 0, 1, 0, 51, 0, 4, 4], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [4, 10, 0, 0, 0, 0, 8, 1, 37, 0], [0, 0, 0, 3, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7143 0.6731 0.6931 52
1 0.7808 0.9500 0.8571 60
2 0.7447 0.6863 0.7143 51
3 0.8095 0.6182 0.7010 55
4 0.9074 0.8750 0.8909 56
5 0.8600 0.7679 0.8113 56
6 0.8095 0.8095 0.8095 63
7 0.8254 0.9286 0.8739 56
8 0.8043 0.6167 0.6981 60
9 0.6829 0.9333 0.7887 60
accuracy 0.7891 569
macro avg 0.7939 0.7858 0.7838 569 weighted avg 0.7942 0.7891 0.7855 569 | | 1.1396 | 6.72 | 900 | 1.0749 | 0.8067 | [[39, 3, 0, 0, 1, 1, 0, 2, 3, 3], [1, 56, 0, 0, 0, 0, 1, 0, 1, 1], [2, 0, 38, 1, 1, 3, 0, 6, 0, 0], [3, 1, 1, 33, 0, 3, 0, 1, 0, 13], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 1, 44, 1, 0, 0, 2], [3, 0, 0, 0, 1, 0, 53, 0, 2, 4], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [5, 9, 0, 0, 0, 0, 8, 1, 37, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 57]] | precision recall f1-score support
0 0.7222 0.7500 0.7358 52
1 0.8000 0.9333 0.8615 60
2 0.7451 0.7451 0.7451 51
3 0.8684 0.6000 0.7097 55
4 0.9259 0.8929 0.9091 56
5 0.8462 0.7857 0.8148 56
6 0.8413 0.8413 0.8413 63
7 0.8254 0.9286 0.8739 56
8 0.8605 0.6167 0.7184 60
9 0.7037 0.9500 0.8085 60
accuracy 0.8067 569
macro avg 0.8139 0.8044 0.8018 569 weighted avg 0.8148 0.8067 0.8033 569 | | 1.0577 | 7.46 | 1000 | 1.0399 | 0.8155 | [[37, 3, 0, 0, 1, 1, 1, 2, 4, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 4, 1, 4, 0, 3, 0, 0], [3, 1, 0, 40, 0, 3, 0, 1, 0, 7], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 6, 1, 1, 45, 1, 0, 0, 2], [3, 0, 0, 2, 1, 0, 53, 0, 2, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [3, 9, 0, 0, 0, 0, 9, 1, 38, 0], [0, 0, 0, 4, 0, 1, 0, 1, 0, 54]] | precision recall f1-score support
0 0.7708 0.7115 0.7400 52
1 0.8028 0.9500 0.8702 60
2 0.7755 0.7451 0.7600 51
3 0.7547 0.7273 0.7407 55
4 0.9259 0.8929 0.9091 56
5 0.8333 0.8036 0.8182 56
6 0.8154 0.8413 0.8281 63
7 0.8667 0.9286 0.8966 56
8 0.8444 0.6333 0.7238 60
9 0.7714 0.9000 0.8308 60
accuracy 0.8155 569
macro avg 0.8161 0.8134 0.8117 569 weighted avg 0.8167 0.8155 0.8130 569 | | 0.9935 | 8.21 | 1100 | 1.0205 | 0.8190 | [[38, 4, 0, 0, 1, 1, 0, 2, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 2, 1, 3, 0, 6, 0, 0], [3, 1, 0, 38, 0, 3, 0, 1, 0, 9], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 2, 44, 0, 0, 0, 2], [3, 0, 0, 2, 1, 0, 54, 0, 2, 1], [0, 0, 2, 1, 0, 0, 0, 53, 0, 0], [2, 10, 0, 0, 0, 0, 9, 1, 38, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7917 0.7308 0.7600 52
1 0.7808 0.9500 0.8571 60
2 0.7755 0.7451 0.7600 51
3 0.8085 0.6909 0.7451 55
4 0.9091 0.8929 0.9009 56
5 0.8462 0.7857 0.8148 56
6 0.8438 0.8571 0.8504 63
7 0.8281 0.9464 0.8833 56
8 0.8636 0.6333 0.7308 60
9 0.7671 0.9333 0.8421 60
accuracy 0.8190 569
macro avg 0.8214 0.8166 0.8145 569 weighted avg 0.8220 0.8190 0.8158 569 | | 1.1058 | 8.96 | 1200 | 1.0022 | 0.8225 | [[38, 3, 0, 0, 2, 1, 1, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 37, 2, 1, 5, 0, 5, 0, 0], [4, 1, 0, 39, 0, 3, 0, 0, 0, 8], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 6, 1, 1, 45, 1, 0, 0, 2], [3, 0, 0, 1, 1, 0, 55, 0, 2, 1], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [3, 9, 0, 0, 0, 0, 9, 0, 39, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7600 0.7308 0.7451 52
1 0.8028 0.9500 0.8702 60
2 0.7708 0.7255 0.7475 51
3 0.8298 0.7091 0.7647 55
4 0.9091 0.8929 0.9009 56
5 0.8182 0.8036 0.8108 56
6 0.8209 0.8730 0.8462 63
7 0.8814 0.9286 0.9043 56
8 0.8667 0.6500 0.7429 60
9 0.7778 0.9333 0.8485 60
accuracy 0.8225 569
macro avg 0.8237 0.8197 0.8181 569 weighted avg 0.8244 0.8225 0.8197 569 | | 1.0422 | 9.7 | 1300 | 0.9962 | 0.8243 | [[39, 3, 0, 0, 2, 1, 0, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 38, 2, 1, 4, 0, 5, 0, 0], [4, 1, 0, 39, 0, 3, 0, 0, 0, 8], [1, 1, 2, 1, 50, 0, 0, 0, 0, 1], [0, 0, 7, 1, 1, 44, 1, 0, 0, 2], [3, 0, 0, 1, 1, 0, 55, 0, 2, 1], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [2, 9, 0, 0, 0, 0, 9, 1, 39, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
0 0.7800 0.7500 0.7647 52
1 0.8028 0.9500 0.8702 60
2 0.7600 0.7451 0.7525 51
3 0.8298 0.7091 0.7647 55
4 0.9091 0.8929 0.9009 56
5 0.8302 0.7857 0.8073 56
6 0.8333 0.8730 0.8527 63
7 0.8667 0.9286 0.8966 56
8 0.8667 0.6500 0.7429 60
9 0.7778 0.9333 0.8485 60
accuracy 0.8243 569
macro avg 0.8256 0.8218 0.8201 569 weighted avg 0.8264 0.8243 0.8216 569 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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