model update

README.md

@@ -18,13 +18,13 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.03130537507383343
+      value: 0.8924985233313645
     - name: F1 (macro)
       type: f1_macro
-      value: 0.011682073096465156
+      value: 0.7744939280307456
     - name: Accuracy
       type: accuracy
-      value: 0.03130537507383343
+      value: 0.8924985233313645
 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_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): 0.03130537507383343
-- F1 (macro): 0.011682073096465156
-- Accuracy: 0.03130537507383343
+- F1 (micro): 0.8924985233313645
+- F1 (macro): 0.7744939280307456
+- Accuracy: 0.8924985233313645
 
 
 ### Usage

metric_summary.json

@@ -1 +1 @@
-{"test/eval_loss": 1.8547085523605347, "test/eval_f1": 0.03130537507383343, "test/eval_f1_macro": 0.011682073096465156, "test/eval_accuracy": 0.03130537507383343, "test/eval_runtime": 53.0779, "test/eval_samples_per_second": 31.897, "test/eval_steps_per_second": 1.997}
+{"test/eval_loss": 0.4388335347175598, "test/eval_f1": 0.8924985233313645, "test/eval_f1_macro": 0.7744939280307456, "test/eval_accuracy": 0.8924985233313645, "test/eval_runtime": 53.4061, "test/eval_samples_per_second": 31.7, "test/eval_steps_per_second": 1.985}