readme: ---
  datasets:
  - cardiffnlp/tweet_topic_single
  metrics:
  - f1
  - accuracy
  model-index:
  - name: cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020
    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: 0.8777318369757826
      - name: F1 (macro)
        type: f1_macro
        value: 0.7461188384572722
      - name: Accuracy
        type: accuracy
        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"
    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-dec2021-tweet-topic-single-2020
  
  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): 0.8777318369757826
  - F1 (macro): 0.7461188384572722
  - Accuracy: 0.8777318369757826
  
  
  ### Usage
  
  ```python
  from transformers import pipeline
  
  pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020")  
  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"
  }
  
  ```
  