model update

README.md

@@ -17,13 +17,13 @@ model-index:
     metrics:
     - name: Micro F1 (tweet_eval/offensive)
       type: micro_f1_tweet_eval/offensive
-      value: 0.8267441860465117
+      value: 0.858139534883721
     - name: Macro F1 (tweet_eval/offensive)
       type: micro_f1_tweet_eval/offensive
-      value: 0.7798247739226207
+      value: 0.8232706055154664
     - name: Accuracy (tweet_eval/offensive)
       type: accuracy_tweet_eval/offensive
-      value: 0.8267441860465117
+      value: 0.858139534883721
 pipeline_tag: text-classification
 widget:
 - text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}
@@ -50,9 +50,9 @@ Training split is `train` and parameters have been tuned on the validation split
 
 Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m-offensive/raw/main/metric.json)).
 
-- F1 (micro): 0.8267441860465117
-- F1 (macro): 0.7798247739226207
-- Accuracy: 0.8267441860465117
+- F1 (micro): 0.858139534883721
+- F1 (macro): 0.8232706055154664
+- Accuracy: 0.858139534883721
 
 ### Usage
 Install tweetnlp via pip.

best_run_hyperparameters.json

@@ -1 +1 @@
-{"learning_rate": 5.399484409787439e-05, "num_train_epochs": 4, "per_device_train_batch_size": 16}
+{"learning_rate": 1.1208547084229388e-05, "num_train_epochs": 2, "per_device_train_batch_size": 32}

metric.json

@@ -1 +1 @@
-{"eval_loss": 0.43852388858795166, "eval_f1": 0.8267441860465117, "eval_f1_macro": 0.7798247739226207, "eval_accuracy": 0.8267441860465117, "eval_runtime": 4.6276, "eval_samples_per_second": 185.84, "eval_steps_per_second": 23.338}
+{"eval_loss": 0.35168176889419556, "eval_f1": 0.858139534883721, "eval_f1_macro": 0.8232706055154664, "eval_accuracy": 0.858139534883721, "eval_runtime": 4.9862, "eval_samples_per_second": 172.477, "eval_steps_per_second": 21.66}