Upload README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- indonlu
+metrics:
+- accuracy
+- f1
+model-index:
+- name: Fine-tuned-Indonesian-Sentiment-Classifier
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: indonlu
+      type: indonlu
+      config: smsa
+      split: validation
+      args: smsa
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9317460317460318
+    - name: F1
+      type: f1
+      value: 0.9034223843742829
+language:
+- id
+pipeline_tag: text-classification
+
+widget:
+- text: "Kalo kamu WFH emang kerja?"
+- text: "dia tidak cantik"
+---
+
+<!-- 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. -->
+
+# Fine-tuned-Indonesian-Sentiment-Classifier
+
+This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the [IndoNLU's SmSA](https://huggingface.co/datasets/indonlp/indonlu) dataset.
+It achieves the following results on the evaluation dataset:
+- Loss: 0.3233
+- Accuracy: 0.9317
+- F1: 0.9034
+
+And the results of the test dataset:
+- Accuracy: 0.928
+- F1 macro: 0.9113470780757361
+- F1 micro: 0.928
+- F1 weighted: 0.9261959965604815
+
+## Model description
+
+This model can be used to determine the sentiment of a text with three possible outputs [positive, negative, or neutral]
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+Pre-trained = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
+tokenizer = AutoTokenizer.from_pretrained(Pre-trained)
+model = AutoModelForSequenceClassification.from_pretrained(Pre-trained)
+```
+### make classification
+```python
+pretrained_name = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
+class_model = pipeline(tokenizer=pretrained_name, model=pretrained_name)
+
+test_data = "buku ini jelek sekali"
+class_model(test_data)
+```
+output: [{'label': 'negative', 'score': 0.9996247291564941}]
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
+| 0.08          | 1.0   | 688   | 0.3532          | 0.9310   | 0.9053 |
+| 0.0523        | 2.0   | 1376  | 0.3233          | 0.9317   | 0.9034 |
+| 0.045         | 3.0   | 2064  | 0.3949          | 0.9286   | 0.8995 |
+| 0.0252        | 4.0   | 2752  | 0.4662          | 0.9310   | 0.9049 |
+| 0.0149        | 5.0   | 3440  | 0.6251          | 0.9246   | 0.8899 |
+| 0.0091        | 6.0   | 4128  | 0.6148          | 0.9254   | 0.8928 |
+| 0.0111        | 7.0   | 4816  | 0.6259          | 0.9222   | 0.8902 |
+| 0.0106        | 8.0   | 5504  | 0.6123          | 0.9238   | 0.8882 |
+| 0.0092        | 9.0   | 6192  | 0.6353          | 0.9230   | 0.8928 |
+| 0.0085        | 10.0  | 6880  | 0.6733          | 0.9254   | 0.8989 |
+| 0.0062        | 11.0  | 7568  | 0.6666          | 0.9302   | 0.9027 |
+| 0.0036        | 12.0  | 8256  | 0.7578          | 0.9230   | 0.8962 |
+| 0.0055        | 13.0  | 8944  | 0.7378          | 0.9270   | 0.8947 |
+| 0.0023        | 14.0  | 9632  | 0.7758          | 0.9230   | 0.8978 |
+| 0.0009        | 15.0  | 10320 | 0.7051          | 0.9278   | 0.9006 |
+| 0.0033        | 16.0  | 11008 | 0.7442          | 0.9214   | 0.8902 |
+| 0.0           | 17.0  | 11696 | 0.7513          | 0.9254   | 0.8974 |
+| 0.0           | 18.0  | 12384 | 0.7554          | 0.9270   | 0.8999 |
+
+Although trained with 18 epochs, this model uses the best weight (Epoch 2)
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3