model update

README.md

@@ -17,13 +17,13 @@ model-index:
     metrics:
     - name: Micro F1 (tweet_eval/offensive)
       type: micro_f1_tweet_eval/offensive
-      value: 0.8511627906976744
+      value: 0.8627906976744186
     - name: Macro F1 (tweet_eval/offensive)
       type: micro_f1_tweet_eval/offensive
-      value: 0.8169044070683416
+      value: 0.8295026881720429
     - name: Accuracy (tweet_eval/offensive)
       type: accuracy_tweet_eval/offensive
-      value: 0.8511627906976744
+      value: 0.8627906976744186
 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-dec2021-offensive/raw/main/metric.json)).
 
-- F1 (micro): 0.8511627906976744
-- F1 (macro): 0.8169044070683416
-- Accuracy: 0.8511627906976744
+- F1 (micro): 0.8627906976744186
+- F1 (macro): 0.8295026881720429
+- Accuracy: 0.8627906976744186
 
 ### Usage
 Install tweetnlp via pip.

best_run_hyperparameters.json

@@ -1 +1 @@
-{"learning_rate": 5.61151641533451e-06, "num_train_epochs": 5, "per_device_train_batch_size": 16}
+{"learning_rate": 1.717762111233842e-05, "num_train_epochs": 2, "per_device_train_batch_size": 16}

metric.json

@@ -1 +1 @@
-{"eval_loss": 0.48542600870132446, "eval_f1": 0.8511627906976744, "eval_f1_macro": 0.8169044070683416, "eval_accuracy": 0.8511627906976744, "eval_runtime": 2.2724, "eval_samples_per_second": 378.458, "eval_steps_per_second": 47.527}
+{"eval_loss": 0.36703118681907654, "eval_f1": 0.8627906976744186, "eval_f1_macro": 0.8295026881720429, "eval_accuracy": 0.8627906976744186, "eval_runtime": 5.1083, "eval_samples_per_second": 168.352, "eval_steps_per_second": 21.142}