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videberta-sentiment-analysis

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9496688741721855
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  - name: Precision
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  type: precision
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- value: 0.9539227895392279
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  - name: Recall
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  type: recall
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- value: 0.9515527950310559
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  - name: F1
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  type: f1
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- value: 0.9527363184079602
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Fsoft-AIC/videberta-xsmall](https://huggingface.co/Fsoft-AIC/videberta-xsmall) on the vietnamese_students_feedback dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2903
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- - Accuracy: 0.9497
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- - Precision: 0.9539
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- - Recall: 0.9516
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- - F1: 0.9527
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  ## Model description
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@@ -78,24 +78,46 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.2029 | 2.91 | 500 | 0.2022 | 0.9358 | 0.9414 | 0.9379 | 0.9396 |
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- | 0.1435 | 5.81 | 1000 | 0.2109 | 0.9325 | 0.9200 | 0.9565 | 0.9379 |
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- | 0.1023 | 8.72 | 1500 | 0.2648 | 0.9344 | 0.9263 | 0.9528 | 0.9394 |
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- | 0.08 | 11.63 | 2000 | 0.2360 | 0.9437 | 0.9455 | 0.9491 | 0.9473 |
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- | 0.0628 | 14.53 | 2500 | 0.2758 | 0.9417 | 0.9377 | 0.9540 | 0.9458 |
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- | 0.0493 | 17.44 | 3000 | 0.3189 | 0.9351 | 0.9223 | 0.9590 | 0.9403 |
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- | 0.0397 | 20.35 | 3500 | 0.3662 | 0.9377 | 0.9257 | 0.9602 | 0.9427 |
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- | 0.0318 | 23.26 | 4000 | 0.2903 | 0.9497 | 0.9539 | 0.9516 | 0.9527 |
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- | 0.0244 | 26.16 | 4500 | 0.3962 | 0.9450 | 0.9381 | 0.9602 | 0.9490 |
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- | 0.0176 | 29.07 | 5000 | 0.3940 | 0.9464 | 0.9425 | 0.9578 | 0.9501 |
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- | 0.0165 | 31.98 | 5500 | 0.3990 | 0.9411 | 0.9486 | 0.9404 | 0.9445 |
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- | 0.0139 | 34.88 | 6000 | 0.4565 | 0.9424 | 0.9336 | 0.9602 | 0.9467 |
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- | 0.0123 | 37.79 | 6500 | 0.3779 | 0.9457 | 0.9491 | 0.9491 | 0.9491 |
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- | 0.0118 | 40.7 | 7000 | 0.4308 | 0.9444 | 0.9380 | 0.9590 | 0.9484 |
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- | 0.0086 | 43.6 | 7500 | 0.4732 | 0.9404 | 0.9344 | 0.9553 | 0.9447 |
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- | 0.0076 | 46.51 | 8000 | 0.4197 | 0.9457 | 0.9547 | 0.9429 | 0.9487 |
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- | 0.0067 | 49.42 | 8500 | 0.4952 | 0.9444 | 0.9391 | 0.9578 | 0.9483 |
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- | 0.0062 | 52.33 | 9000 | 0.4907 | 0.9437 | 0.9444 | 0.9503 | 0.9474 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9470198675496688
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  - name: Precision
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  type: precision
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+ value: 0.9480840543881335
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  - name: Recall
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  type: recall
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+ value: 0.9527950310559006
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  - name: F1
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  type: f1
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+ value: 0.9504337050805451
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [Fsoft-AIC/videberta-xsmall](https://huggingface.co/Fsoft-AIC/videberta-xsmall) on the vietnamese_students_feedback dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2787
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+ - Accuracy: 0.9470
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+ - Precision: 0.9481
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+ - Recall: 0.9528
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+ - F1: 0.9504
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.6152 | 0.58 | 100 | 0.4777 | 0.8007 | 0.8580 | 0.7503 | 0.8005 |
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+ | 0.408 | 1.16 | 200 | 0.3241 | 0.8669 | 0.8943 | 0.8509 | 0.8721 |
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+ | 0.3268 | 1.74 | 300 | 0.2726 | 0.8954 | 0.8837 | 0.9255 | 0.9041 |
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+ | 0.2654 | 2.33 | 400 | 0.2296 | 0.9199 | 0.9212 | 0.9292 | 0.9252 |
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+ | 0.253 | 2.91 | 500 | 0.2088 | 0.9159 | 0.9206 | 0.9217 | 0.9212 |
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+ | 0.2014 | 3.49 | 600 | 0.2318 | 0.9172 | 0.9028 | 0.9466 | 0.9242 |
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+ | 0.1939 | 4.07 | 700 | 0.2131 | 0.9212 | 0.9224 | 0.9304 | 0.9264 |
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+ | 0.1698 | 4.65 | 800 | 0.2005 | 0.9311 | 0.9499 | 0.9193 | 0.9343 |
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+ | 0.1822 | 5.23 | 900 | 0.2249 | 0.9245 | 0.9089 | 0.9540 | 0.9309 |
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+ | 0.1441 | 5.81 | 1000 | 0.2038 | 0.9311 | 0.9311 | 0.9404 | 0.9357 |
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+ | 0.1403 | 6.4 | 1100 | 0.2044 | 0.9338 | 0.9315 | 0.9453 | 0.9383 |
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+ | 0.1377 | 6.98 | 1200 | 0.1991 | 0.9417 | 0.9567 | 0.9329 | 0.9447 |
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+ | 0.1191 | 7.56 | 1300 | 0.2955 | 0.9119 | 0.8792 | 0.9677 | 0.9213 |
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+ | 0.1227 | 8.14 | 1400 | 0.2362 | 0.9318 | 0.9199 | 0.9553 | 0.9372 |
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+ | 0.1023 | 8.72 | 1500 | 0.2221 | 0.9358 | 0.9286 | 0.9528 | 0.9405 |
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+ | 0.1049 | 9.3 | 1600 | 0.1940 | 0.9424 | 0.9454 | 0.9466 | 0.9460 |
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+ | 0.1002 | 9.88 | 1700 | 0.1949 | 0.9404 | 0.9649 | 0.9217 | 0.9428 |
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+ | 0.0946 | 10.47 | 1800 | 0.2232 | 0.9404 | 0.9625 | 0.9242 | 0.9430 |
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+ | 0.0911 | 11.05 | 1900 | 0.2016 | 0.9457 | 0.9641 | 0.9329 | 0.9482 |
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+ | 0.0818 | 11.63 | 2000 | 0.2636 | 0.9311 | 0.9128 | 0.9627 | 0.9371 |
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+ | 0.0889 | 12.21 | 2100 | 0.2279 | 0.9450 | 0.9524 | 0.9441 | 0.9482 |
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+ | 0.0668 | 12.79 | 2200 | 0.2460 | 0.9411 | 0.9409 | 0.9491 | 0.9450 |
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+ | 0.0635 | 13.37 | 2300 | 0.2764 | 0.9424 | 0.9465 | 0.9453 | 0.9459 |
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+ | 0.072 | 13.95 | 2400 | 0.2519 | 0.9437 | 0.9390 | 0.9565 | 0.9477 |
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+ | 0.0697 | 14.53 | 2500 | 0.2705 | 0.9404 | 0.9408 | 0.9478 | 0.9443 |
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+ | 0.0602 | 15.12 | 2600 | 0.2686 | 0.9450 | 0.9513 | 0.9453 | 0.9483 |
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+ | 0.065 | 15.7 | 2700 | 0.2629 | 0.9450 | 0.9501 | 0.9466 | 0.9484 |
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+ | 0.0628 | 16.28 | 2800 | 0.2644 | 0.9450 | 0.9547 | 0.9416 | 0.9481 |
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+ | 0.0505 | 16.86 | 2900 | 0.2704 | 0.9424 | 0.9400 | 0.9528 | 0.9463 |
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+ | 0.0471 | 17.44 | 3000 | 0.2787 | 0.9470 | 0.9481 | 0.9528 | 0.9504 |
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+ | 0.0568 | 18.02 | 3100 | 0.2766 | 0.9450 | 0.9424 | 0.9553 | 0.9488 |
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+ | 0.0523 | 18.6 | 3200 | 0.2659 | 0.9424 | 0.9421 | 0.9503 | 0.9462 |
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+ | 0.0487 | 19.19 | 3300 | 0.3091 | 0.9338 | 0.9222 | 0.9565 | 0.9390 |
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+ | 0.0529 | 19.77 | 3400 | 0.3575 | 0.9272 | 0.9045 | 0.9652 | 0.9339 |
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+ | 0.0484 | 20.35 | 3500 | 0.3228 | 0.9358 | 0.9214 | 0.9615 | 0.9410 |
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+ | 0.0456 | 20.93 | 3600 | 0.2694 | 0.9437 | 0.9412 | 0.9540 | 0.9476 |
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+ | 0.0424 | 21.51 | 3700 | 0.2793 | 0.9404 | 0.9376 | 0.9516 | 0.9445 |
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+ | 0.045 | 22.09 | 3800 | 0.2953 | 0.9417 | 0.9356 | 0.9565 | 0.9459 |
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+ | 0.0395 | 22.67 | 3900 | 0.2840 | 0.9417 | 0.9377 | 0.9540 | 0.9458 |
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+ | 0.0418 | 23.26 | 4000 | 0.3527 | 0.9305 | 0.9108 | 0.9640 | 0.9366 |
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  ### Framework versions