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README.md

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----
-license: mit
----
+---
+widget:
+- text: >-
+    Gapapa kalian gak tahu band Indo ini. Tapi jangan becanda. Karena mereka
+    berani menyanyikan dengan lantang bagaimana aktivis ditikam, diracun,
+    dikursilitrikkan, dan dibunuh di udara. Orang-orang yang berkorban nyawa
+    supaya kalian menikmati hari ini sambil ngetwit tanpa khawatir
+  example_title: Example 1
+  output:
+  - label: Negative
+    score: 0.2964
+  - label: Neutral
+    score: 0.067
+  - label: Positive
+    score: 0.6969
+- text: >-
+    Selama ada kelompok yg ingin jd mesias, selama itu jg govt punya justifikasi
+    but bikin banyak aturan = celah korup/power abuse. Keadilan adalah
+    deregulasi.
+  example_title: Example 2
+  output:
+  - label: Negative
+    score: 0.971
+  - label: Neutral
+    score: 0.0165
+  - label: Positive
+    score: 0.126
+- text: >-
+    saat pendukungmu oke😹 gas ✌🏽oke😹 gas ✌🏽tapi kamu malah ketawa 🤣 itu ga
+    respek 😠banget wok jangan lupa makan siang 😁geratisnya wok😋😹✌🏽
+  example_title: Example 3
+  output:
+  - label: Negative
+    score: 0.6457
+  - label: Neutral
+    score: 0.048
+  - label: Positive
+    score: 0.3063
+- text: >-
+    Infoin loker wfh/freelance untuk mahasiswa dong, pengin bangget buat
+    tambahan uang jajan di kos
+  example_title: Example 4
+  output:
+  - label: Negative
+    score: 0.0544
+  - label: Neutral
+    score: 0.6973
+  - label: Positive
+    score: 0.2482
+- text: >-
+    Cari kerja sekarang tuh susah. Anaknya Presiden aja mesti dicariin kerjaan
+    sama bapaknya
+  example_title: Example 5
+  output:
+  - label: Negative
+    score: 0.9852
+  - label: Neutral
+    score: 0.0116
+  - label: Positive
+    score: 0.0032
+- text: >-
+    Komisi Penyiaran Indonesia (KPI) meminta agar tayangan televisi menampilkan
+    citra positif Polri secara edukatif dan akurat. Hal ini disampaikan ketua
+    KPI Pusat Ubaidillah dalam sebuah diskusi panel
+  example_title: Example 6 
+  output:
+  - label: Neutral
+    score: 0.9932
+  - label: Positive
+    score: 0.0063
+  - label: Negative
+    score: 0.0005
+- text: >-
+    Jgnkan tweet becandaan.. kadang tweet normal yg gue baca 'oh menarik' trs
+    gue like/retweet, trs gue tinggal tidur, BESOKNYA ITU TWEET DIRUJAK. Gue jadi
+    mikir, ini emang gue yang merasa semua hal menarik dan semua org bisa aja
+    bener.. ATAU.. SEMUA ORANG jadi sensitif
+  example_title: Example 7 
+  output:
+  - label: Negative
+    score: 0.5531
+  - label: Neutral
+    score: 0.4426
+  - label: Positive
+    score: 0.0043
+
+library_name: transformers
+license: mit
+language:
+- id
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+## Model Details
+
+### Model Description
+This model is a fine-tuned version of [IndoBertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) for Indonesian sentiment analysis. The model is designed to classify sentiment into three categories: negative, positive, and neutral. It was trained on a diverse dataset comprising reactions from Twitter and other social media platforms, covering various topics, including politics, disasters, and education. The model is optimized using Optuna for hyperparameter tuning and evaluated using accuracy, F1-score, precision, and recall metrics.
+
+## Bias and Limitations
+Do consider that this model is trained using certain data, which may cause bias in the sentiment classification process. The model may inherit socio-cultural biases from its training data and may be less accurate for the most recent events that are not covered in the data. The limitation of the three categories may also not fully grasp the complexity of emotions, especially in capturing particular contexts. Therefore, it is important to consider and account for such biases when using this model.
+
+## Evaluation Results
+The training process uses hyperparameter optimization techniques with Optuna. The model was trained for a maximum of 10 epochs with a batch size of 16, using an optimized learning rate and weight decay. The evaluation strategy is performed every 100 steps, saving the best model based on accuracy. The training also applied early stopping with patience 3 to prevent overfitting.
+
+<table style="text-align: center; width: 100%;">
+  <tr>
+    <th>Epoch</th>
+    <th>Training Loss</th>
+    <th>Validation Loss</th>
+    <th>Accuracy</th>
+    <th>F1</th>
+    <th>Precision</th>
+    <th>Recall</th>
+  </tr>
+  <tr>
+    <td>100</td>
+    <td>1.052800</td>
+    <td>0.995017</td>
+    <td>0.482368</td>
+    <td>0.348356</td>
+    <td>0.580544</td>
+    <td>0.482368</td>
+  </tr>
+  <tr>
+    <td>200</td>
+    <td>0.893700</td>
+    <td>0.807756</td>
+    <td>0.730479</td>
+    <td>0.703134</td>
+    <td>0.756189</td>
+    <td>0.730479</td>
+  </tr>
+  <tr>
+    <td>300</td>
+    <td>0.583400</td>
+    <td>0.476157</td>
+    <td>0.850126</td>
+    <td>0.847161</td>
+    <td>0.849467</td>
+    <td>0.850126</td>
+  </tr>
+  <tr>
+    <td>400</td>
+    <td>0.413600</td>
+    <td>0.385942</td>
+    <td>0.867758</td>
+    <td>0.867614</td>
+    <td>0.870417</td>
+    <td>0.867758</td>
+  </tr>
+  <tr>
+    <td>500</td>
+    <td>0.345700</td>
+    <td>0.362191</td>
+    <td>0.885390</td>
+    <td>0.883918</td>
+    <td>0.886880</td>
+    <td>0.885390</td>
+  </tr>
+  <tr>
+    <td>600</td>
+    <td>0.245400</td>
+    <td>0.330090</td>
+    <td>0.897985</td>
+    <td>0.897466</td>
+    <td>0.897541</td>
+    <td>0.897985</td>
+  </tr>
+  <tr>
+    <td>700</td>
+    <td>0.485000</td>
+    <td>0.308807</td>
+    <td>0.899244</td>
+    <td>0.898736</td>
+    <td>0.898761</td>
+    <td>0.899244</td>
+  </tr>
+  <tr>
+    <td>800</td>
+    <td>0.363700</td>
+    <td>0.328786</td>
+    <td>0.896725</td>
+    <td>0.895167</td>
+    <td>0.898695</td>
+    <td>0.896725</td>
+  </tr>
+  <tr>
+    <td>900</td>
+    <td>0.369800</td>
+    <td>0.329429</td>
+    <td>0.892947</td>
+    <td>0.893138</td>
+    <td>0.898281</td>
+    <td>0.892947</td>
+  </tr>
+  <tr>
+    <td>1000</td>
+    <td>0.273300</td>
+    <td>0.305412</td>
+    <td>0.910579</td>
+    <td>0.910355</td>
+    <td>0.910519</td>
+    <td>0.910579</td>
+  </tr>
+  <tr>
+    <td>1100</td>
+    <td>0.272800</td>
+    <td>0.388976</td>
+    <td>0.891688</td>
+    <td>0.893113</td>
+    <td>0.896606</td>
+    <td>0.891688</td>
+  </tr>
+  <tr>
+    <td>1200</td>
+    <td>0.259900</td>
+    <td>0.305771</td>
+    <td>0.913098</td>
+    <td>0.913123</td>
+    <td>0.913669</td>
+    <td>0.913098</td>
+  </tr>
+  <tr>
+    <td>1300</td>
+    <td>0.293500</td>
+    <td>0.317654</td>
+    <td>0.908060</td>
+    <td>0.908654</td>
+    <td>0.909939</td>
+    <td>0.908060</td>
+  </tr>
+  <tr>
+    <td>1400</td>
+    <td>0.255200</td>
+    <td>0.331161</td>
+    <td>0.915617</td>
+    <td>0.915708</td>
+    <td>0.916149</td>
+    <td>0.915617</td>
+  </tr>
+  <tr>
+    <td>1500</td>
+    <td>0.139800</td>
+    <td>0.352545</td>
+    <td>0.909320</td>
+    <td>0.909768</td>
+    <td>0.911014</td>
+    <td>0.909320</td>
+  </tr>
+  <tr>
+    <td>1600</td>
+    <td>0.194400</td>
+    <td>0.372482</td>
+    <td>0.904282</td>
+    <td>0.904296</td>
+    <td>0.906285</td>
+    <td>0.904282</td>
+  </tr>
+  <tr>
+    <td>1700</td>
+    <td>0.134200</td>
+    <td>0.340576</td>
+    <td>0.906801</td>
+    <td>0.907110</td>
+    <td>0.907780</td>
+    <td>0.906801</td>
+  </tr>
+</table>
+
+## Citation 
+```
+@misc{Ardiyanto_Mikhael_2024,
+    author    = {Mikhael Ardiyanto},
+    title     = {Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis},
+    year      = {2024},
+    URL       = {https://huggingface.co/Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis},
+    publisher = {Hugging Face}
+}

config.json

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+{
+  "_name_or_path": "/content/drive/My Drive/Sentiment-3/model-NusaBERT-sentiment",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.2,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_ids": 0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.2,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Negative",
+    "1": "Neutral",
+    "2": "Positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Negative": 0,
+    "Neutral": 1,
+    "Positive": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31923
+}

model.safetensors

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+oid sha256:37d7220817bf9603a095a9b465abe6c59fd0aa40afb9f6ecc79fb584a3a51659
+size 442265596

special_tokens_map.json

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+{
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+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "tokenizer_class": "BertTokenizer",
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+}