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README.md

@@ -1,17 +1,18 @@
+
 ---
-datasets:
-- tyqiangz/multilingual-sentiments
 language:
 - en
 - ms
 - zh
-license: apache-2.0
-metrics:
-- accuracy
 tags:
 - sentiment-analysis
 - text-classification
 - multilingual
+license: apache-2.0
+datasets:
+- tyqiangz/multilingual-sentiments
+metrics:
+- accuracy
 model-index:
 - name: xlm-roberta-base-sentiment-multilingual-finetuned
   results:
@@ -19,11 +20,68 @@ model-index:
       type: text-classification
       name: Text Classification
     dataset:
-      name: Multilingual Sentiments
       type: tyqiangz/multilingual-sentiments
+      name: Multilingual Sentiments
     metrics:
     - type: accuracy
-      value: 0.7737435897435897
+      value: 0.7528205128205128
+
+
+Baseline Scores:
+  Classification Report:
+    Negative:
+      Precision: 0.6153
+      Recall: 0.8292
+      F1-score: 0.7064
+      Support: 1680
+    Neutral:
+      Precision: 0.5381
+      Recall: 0.3035
+      F1-score: 0.3881
+      Support: 1443
+    Positive:
+      Precision: 0.7607
+      Recall: 0.7803
+      F1-score: 0.7704
+      Support: 1752
+  Metrics:
+    Accuracy:
+      Value: 0.6560
+      Support: 4875
+    Macro Avg:
+      Value: 0.6380
+      Support: 4875
+    Weighted Avg:
+      Value: 0.6447
+      Support: 4875
+
+Finetuned Scores:
+  Classification Report:
+    Negative:
+      Precision: 0.7487
+      Recall: 0.7875
+      F1-score: 0.7676
+      Support: 1680
+    Neutral:
+      Precision: 0.6775
+      Recall: 0.6216
+      F1-score: 0.6484
+      Support: 1443
+    Positive:
+      Precision: 0.8128
+      Recall: 0.8276
+      F1-score: 0.8201
+      Support: 1752
+  Metrics:
+    Accuracy:
+      Value: 0.7528
+      Support: 4875
+    Macro Avg:
+      Value: 0.7463
+      Support: 4875
+    Weighted Avg:
+      Value: 0.7507
+      Support: 4875
 ---
 
 # xlm-roberta-base-sentiment-multilingual-finetuned
@@ -46,7 +104,7 @@ The model was fine-tuned using the Hugging Face Transformers library.
 
 training_args = TrainingArguments(
     output_dir="./results",
-    num_train_epochs=1,
+    num_train_epochs=5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=64,
     warmup_steps=500,
@@ -60,7 +118,10 @@ training_args = TrainingArguments(
 
 ## Evaluation results
 
-'eval_accuracy': 0.7737435897435897, 'eval_f1': 0.7724731131078052, 'eval_precision': 0.7733524717389839, 'eval_recall': 0.7737435897435897,
+'eval_accuracy': 0.7528205128205128, 'eval_f1': 0.7511924805177581, 'eval_precision': 0.7506612130427309, 'eval_recall': 0.7528205128205128
+
+
+## Test Score : 
 
 ## Environmental impact