Model save

README.md

@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7068
-- Accuracy: 0.77
-- Precision: 0.77
-- Recall: 0.77
-- F1: 0.7700
+- Loss: 0.5388
+- Accuracy: 0.8311
+- Precision: 0.8311
+- Recall: 0.8311
+- F1: 0.8311
 
 ## Model description
 
@@ -55,13 +55,13 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| No log        | 1.0   | 151  | 0.6582          | 0.6967   | 0.6967    | 0.6967 | 0.6967 |
-| No log        | 2.0   | 302  | 0.7068          | 0.77     | 0.77      | 0.77   | 0.7700 |
+| No log        | 1.0   | 149  | 0.4098          | 0.8277   | 0.8277    | 0.8277 | 0.8277 |
+| No log        | 2.0   | 298  | 0.5388          | 0.8311   | 0.8311    | 0.8311 | 0.8311 |
 
 
 ### Framework versions
 
-- Transformers 4.41.1
+- Transformers 4.41.2
 - Pytorch 2.3.0+cu121
 - Datasets 2.19.1
 - Tokenizers 0.19.1

config.json

@@ -11,16 +11,14 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "Negatif",
-    "1": "Netral",
-    "2": "Positif"
+    "0": "negative",
+    "1": "positive"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "Negatif": 0,
-    "Netral": 1,
-    "Positif": 2
+    "negative": 0,
+    "positive": 1
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
@@ -37,7 +35,7 @@
   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
-  "transformers_version": "4.41.1",
+  "transformers_version": "4.41.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 50000

model.safetensors

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training_args.bin

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