update model card README.md

README.md

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+---
+base_model: Fsoft-AIC/videberta-xsmall
+tags:
+- generated_from_trainer
+datasets:
+- vietnamese_students_feedback
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: videberta-sentiment-analysis
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: vietnamese_students_feedback
+      type: vietnamese_students_feedback
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9496688741721855
+    - name: Precision
+      type: precision
+      value: 0.9539227895392279
+    - name: Recall
+      type: recall
+      value: 0.9515527950310559
+    - name: F1
+      type: f1
+      value: 0.9527363184079602
+---
+
+<!-- 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. -->
+
+# videberta-sentiment-analysis
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.2903
+- Accuracy: 0.9497
+- Precision: 0.9539
+- Recall: 0.9516
+- F1: 0.9527
+
+## 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: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.2029        | 2.91  | 500  | 0.2022          | 0.9358   | 0.9414    | 0.9379 | 0.9396 |
+| 0.1435        | 5.81  | 1000 | 0.2109          | 0.9325   | 0.9200    | 0.9565 | 0.9379 |
+| 0.1023        | 8.72  | 1500 | 0.2648          | 0.9344   | 0.9263    | 0.9528 | 0.9394 |
+| 0.08          | 11.63 | 2000 | 0.2360          | 0.9437   | 0.9455    | 0.9491 | 0.9473 |
+| 0.0628        | 14.53 | 2500 | 0.2758          | 0.9417   | 0.9377    | 0.9540 | 0.9458 |
+| 0.0493        | 17.44 | 3000 | 0.3189          | 0.9351   | 0.9223    | 0.9590 | 0.9403 |
+| 0.0397        | 20.35 | 3500 | 0.3662          | 0.9377   | 0.9257    | 0.9602 | 0.9427 |
+| 0.0318        | 23.26 | 4000 | 0.2903          | 0.9497   | 0.9539    | 0.9516 | 0.9527 |
+| 0.0244        | 26.16 | 4500 | 0.3962          | 0.9450   | 0.9381    | 0.9602 | 0.9490 |
+| 0.0176        | 29.07 | 5000 | 0.3940          | 0.9464   | 0.9425    | 0.9578 | 0.9501 |
+| 0.0165        | 31.98 | 5500 | 0.3990          | 0.9411   | 0.9486    | 0.9404 | 0.9445 |
+| 0.0139        | 34.88 | 6000 | 0.4565          | 0.9424   | 0.9336    | 0.9602 | 0.9467 |
+| 0.0123        | 37.79 | 6500 | 0.3779          | 0.9457   | 0.9491    | 0.9491 | 0.9491 |
+| 0.0118        | 40.7  | 7000 | 0.4308          | 0.9444   | 0.9380    | 0.9590 | 0.9484 |
+| 0.0086        | 43.6  | 7500 | 0.4732          | 0.9404   | 0.9344    | 0.9553 | 0.9447 |
+| 0.0076        | 46.51 | 8000 | 0.4197          | 0.9457   | 0.9547    | 0.9429 | 0.9487 |
+| 0.0067        | 49.42 | 8500 | 0.4952          | 0.9444   | 0.9391    | 0.9578 | 0.9483 |
+| 0.0062        | 52.33 | 9000 | 0.4907          | 0.9437   | 0.9444    | 0.9503 | 0.9474 |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3