model update

README.md

@@ -18,13 +18,13 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.07028942705256941
+      value: 0.896042528056704
     - name: F1 (macro)
       type: f1_macro
-      value: 0.04483451520936405
+      value: 0.8000614127334341
     - name: Accuracy
       type: accuracy
-      value: 0.07028942705256941
+      value: 0.896042528056704
 pipeline_tag: text-classification
 widget:
 - text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
@@ -37,9 +37,9 @@ widget:
 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
 Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
 
-- F1 (micro): 0.07028942705256941
-- F1 (macro): 0.04483451520936405
-- Accuracy: 0.07028942705256941
+- F1 (micro): 0.896042528056704
+- F1 (macro): 0.8000614127334341
+- Accuracy: 0.896042528056704
 
 
 ### Usage

metric_summary.json

@@ -1 +1 @@
-{"test/eval_loss": 1.8398139476776123, "test/eval_f1": 0.07028942705256941, "test/eval_f1_macro": 0.04483451520936405, "test/eval_accuracy": 0.07028942705256941, "test/eval_runtime": 30.8871, "test/eval_samples_per_second": 54.813, "test/eval_steps_per_second": 3.432}
+{"test/eval_loss": 0.7315174341201782, "test/eval_f1": 0.896042528056704, "test/eval_f1_macro": 0.8000614127334341, "test/eval_accuracy": 0.896042528056704, "test/eval_runtime": 13.4045, "test/eval_samples_per_second": 126.3, "test/eval_steps_per_second": 15.816}