model update

README.md

@@ -17,13 +17,13 @@ model-index:
     metrics:
     - name: Micro F1 (tweet_eval/irony)
       type: micro_f1_tweet_eval/irony
-      value: 0.7959183673469388
+      value: 0.7831632653061223
     - name: Macro F1 (tweet_eval/irony)
       type: micro_f1_tweet_eval/irony
-      value: 0.791350632069195
+      value: 0.7772250748823277
     - name: Accuracy (tweet_eval/irony)
       type: accuracy_tweet_eval/irony
-      value: 0.7959183673469388
+      value: 0.7831632653061225
 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-irony/raw/main/metric.json)).
 
-- F1 (micro): 0.7959183673469388
-- F1 (macro): 0.791350632069195
-- Accuracy: 0.7959183673469388
+- F1 (micro): 0.7831632653061223
+- F1 (macro): 0.7772250748823277
+- Accuracy: 0.7831632653061225
 
 ### Usage
 Install tweetnlp via pip.

best_run_hyperparameters.json

@@ -1 +1 @@
-{"learning_rate": 1.5930522616241033e-05, "num_train_epochs": 4, "per_device_train_batch_size": 16}
+{"learning_rate": 2.910635913133073e-05, "num_train_epochs": 5, "per_device_train_batch_size": 4}

metric.json

@@ -1 +1 @@
-{"eval_loss": 1.3228046894073486, "eval_f1": 0.7959183673469388, "eval_f1_macro": 0.791350632069195, "eval_accuracy": 0.7959183673469388, "eval_runtime": 2.2267, "eval_samples_per_second": 352.084, "eval_steps_per_second": 44.01}
+{"eval_loss": 1.3212143182754517, "eval_f1": 0.7831632653061223, "eval_f1_macro": 0.7772250748823277, "eval_accuracy": 0.7831632653061225, "eval_runtime": 2.0561, "eval_samples_per_second": 381.312, "eval_steps_per_second": 47.664}