Update README.md

README.md

@@ -51,7 +51,14 @@ model-index:
       name: F1
 ---
 
-# SetFit with firqaaa/indo-sentence-bert-base
+# SetFit with firqaaa/indo-sentence-bert-base for indonlu/smsa
+
+## Author
+
+**Kelompok 3 :**
+- Muhammad Guntur Arfianto (20/459272/PA/19933)
+- Putri Iqlima Miftahuddini (23/531392/NUGM/01467)
+- Alan Kurniawan (23/531301/NUGM/01382)
 
 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
 
@@ -60,6 +67,8 @@ The model has been trained using an efficient few-shot learning technique that i
 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
 2. Training a classification head with features from the fine-tuned Sentence Transformer.
 
+The dataset that was used for fine-tuning this model is [indonlu](https://huggingface.co/datasets/indonlp/indonlu), specifically its subset, SmSa dataset.
+
 ## Model Details
 
 ### Model Description
@@ -140,9 +149,6 @@ preds = model("liverpool sukses di kandang tottenham")
 ## Training Details
 
 ### Training Set Metrics
-| Training set | Min | Median  | Max |
-|:-------------|:----|:--------|:----|
-| Word count   | 1   | 21.4792 | 64  |
 
 | Label | Training Sample Count |
 |:------|:----------------------|
@@ -167,7 +173,7 @@ preds = model("liverpool sukses di kandang tottenham")
 - eval_max_steps: -1
 - load_best_model_at_end: True
 
-### Training Results
+### Training Results (Epoch-to-epoch)
 | Epoch   | Step   | Training Loss | Validation Loss |
 |:-------:|:------:|:-------------:|:---------------:|
 | **1.0** | **96** | **0.0009**    | **0.1923**      |