Delete 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
-
-modelName = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
-tokenizer = AutoTokenizer.from_pretrained(modelName)
-model = AutoModelForSequenceClassification.from_pretrained(modelName)
-```
-### make classification
-```python
-from transformers import pipeline
-modelName = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
-class_model = pipeline(tokenizer=modelName, model=modelName)
-
-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