model update

README.md

@@ -18,13 +18,13 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.10513880685174248
+      value: 0.8777318369757826
     - name: F1 (macro)
       type: f1_macro
-      value: 0.035785101822079314
+      value: 0.7461188384572722
     - name: Accuracy
       type: accuracy
-      value: 0.10513880685174247
+      value: 0.8777318369757826
 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 [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_2020` 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): 0.10513880685174248
-- F1 (macro): 0.035785101822079314
-- Accuracy: 0.10513880685174247
+- F1 (micro): 0.8777318369757826
+- F1 (macro): 0.7461188384572722
+- Accuracy: 0.8777318369757826
 
 
 ### Usage

metric_summary.json

@@ -1 +1 @@
-{"test/eval_loss": 2.080143928527832, "test/eval_f1": 0.10513880685174248, "test/eval_f1_macro": 0.035785101822079314, "test/eval_accuracy": 0.10513880685174247, "test/eval_runtime": 17.589, "test/eval_samples_per_second": 96.253, "test/eval_steps_per_second": 12.053}
+{"test/eval_loss": 0.6739714741706848, "test/eval_f1": 0.8777318369757826, "test/eval_f1_macro": 0.7461188384572722, "test/eval_accuracy": 0.8777318369757826, "test/eval_runtime": 14.1281, "test/eval_samples_per_second": 119.832, "test/eval_steps_per_second": 15.006}