model update

README.md

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+---
+datasets:
+- cardiffnlp/tweet_topic_single
+metrics:
+- f1
+- accuracy
+model-index:
+- name: cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: cardiffnlp/tweet_topic_single
+      type: cardiffnlp/tweet_topic_single
+      args: cardiffnlp/tweet_topic_single
+      split: test_2021 
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.3963378617838157
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.09461364918217709
+    - name: Accuracy
+      type: accuracy
+      value: 0.3963378617838157
+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"
+  example_title: "Example 1"
+- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US." 
+  example_title: "Example 2"
+---
+# cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) 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): 0.3963378617838157
+- F1 (macro): 0.09461364918217709
+- Accuracy: 0.3963378617838157
+
+
+### Usage
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all")  
+topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
+print(topic)
+```
+
+### Reference
+```
+
+@inproceedings{dimosthenis-etal-2022-twitter,
+    title = "{T}witter {T}opic {C}lassification",
+    author = "Antypas, Dimosthenis  and
+    Ushio, Asahi  and
+    Camacho-Collados, Jose  and
+    Neves, Leonardo  and
+    Silva, Vitor  and
+    Barbieri, Francesco",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics"
+}
+
+```

metric_summary.json

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+{"test/eval_loss": 1.6966838836669922, "test/eval_f1": 0.3963378617838157, "test/eval_f1_macro": 0.09461364918217709, "test/eval_accuracy": 0.3963378617838157, "test/eval_runtime": 18.3065, "test/eval_samples_per_second": 92.481, "test/eval_steps_per_second": 11.581}