model update

README.md

@@ -18,13 +18,13 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.05197873597164796
+      value: 0.8824571766095688
     - name: F1 (macro)
       type: f1_macro
-      value: 0.016470147857009173
+      value: 0.7401873227149222
     - name: Accuracy
       type: accuracy
-      value: 0.05197873597164796
+      value: 0.8824571766095688
 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-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) 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): 0.05197873597164796
-- F1 (macro): 0.016470147857009173
-- Accuracy: 0.05197873597164796
+- F1 (micro): 0.8824571766095688
+- F1 (macro): 0.7401873227149222
+- Accuracy: 0.8824571766095688
 
 
 ### Usage

metric_summary.json

@@ -1 +1 @@
-{"test/eval_loss": 1.917781949043274, "test/eval_f1": 0.05197873597164796, "test/eval_f1_macro": 0.016470147857009173, "test/eval_accuracy": 0.05197873597164796, "test/eval_runtime": 55.9561, "test/eval_samples_per_second": 30.256, "test/eval_steps_per_second": 1.894}
+{"test/eval_loss": 0.6086769104003906, "test/eval_f1": 0.8824571766095688, "test/eval_f1_macro": 0.7401873227149222, "test/eval_accuracy": 0.8824571766095688, "test/eval_runtime": 53.4778, "test/eval_samples_per_second": 31.658, "test/eval_steps_per_second": 1.982}