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# Action_agent

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/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        60

    accuracy                         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

কথা_বলা কম্পিউটার_ব্যবহার_করা খাওয়া খেলা_করা ঘুমানো পান_করা পড়া রান্না_করা লেখা হাঁটা