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README.md

@@ -1,19 +1,29 @@
+
 ---
-datasets:
-- tyqiangz/multilingual-sentiments
-- scfengv/TVL_Sentiment_Analysis
-- argilla/twitter-coronavirus
 language:
 - en
 - ms
 - zh
-license: apache-2.0
-metrics:
-- accuracy
 tags:
 - sentiment-analysis
 - text-classification
 - multilingual
+license: apache-2.0
+datasets:
+- tyqiangz/multilingual-sentiments
+- scfengv/TVL_Sentiment_Analysis
+- argilla/twitter-coronavirus
+metrics:
+- accuracy
+model-index:
+- name: xlm-roberta-base-sentiment-multilingual-finetuned
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    metrics:
+    - type: accuracy
+      value: 0.8444
 ---
 # xlm-roberta-base-sentiment-multilingual-finetuned
 
@@ -49,8 +59,9 @@ training_args = TrainingArguments(
 
 ## Evaluation results
 
-Test results: {'eval_loss': 0.5399289727210999, 'eval_accuracy': 0.8276255707762558, 'eval_f1': 0.8264331626181614, 'eval_precision': 0.8267764353557178, 'eval_recall': 0.8276255707762558, 'eval_runtime': 11.3723, 'eval_samples_per_second': 231.087, 'eval_steps_per_second': 3.693, 'epoch': 2.0}
+Test results: {'eval_loss': 0.5881872177124023, 'eval_accuracy': 0.8443683409436834, 'eval_f1': 0.8438625655671501, 'eval_precision': 0.8438352235376211, 'eval_recall': 0.8443683409436834}
 
 ## Environmental impact
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+