readme: ---
  datasets:
  - tweet_eval
  metrics:
  - f1
  - accuracy
  model-index:
  - name: cardiffnlp/twitter-roberta-base-2021-124m-sentiment
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: tweet_eval
        type: sentiment
        split: test 
      metrics:
      - name: Micro F1 (tweet_eval/sentiment)
        type: micro_f1_tweet_eval/sentiment
        value: 0.7133669814392706
      - name: Macro F1 (tweet_eval/sentiment)
        type: micro_f1_tweet_eval/sentiment
        value: 0.7158353597305398
      - name: Accuracy (tweet_eval/sentiment)
        type: accuracy_tweet_eval/sentiment
        value: 0.7133669814392706
  pipeline_tag: text-classification
  widget:
  - text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}
    example_title: "topic_classification 1" 
  - text: Yes, including Medicare and social security saving👍
    example_title: "sentiment 1" 
  - text: All two of them taste like ass.
    example_title: "offensive 1" 
  - text: If you wanna look like a badass, have drama on social media
    example_title: "irony 1" 
  - text: Whoever just unfollowed me you a bitch
    example_title: "hate 1" 
  - text: I love swimming for the same reason I love meditating...the feeling of weightlessness.
    example_title: "emotion 1" 
  - text: Beautiful sunset last night from the pontoon @TupperLakeNY
    example_title: "emoji 1" 
  ---
  # cardiffnlp/twitter-roberta-base-2021-124m-sentiment 
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2021-124m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) on the 
  [`tweet_eval (sentiment)`](https://huggingface.co/datasets/tweet_eval) 
  via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp).
  Training split is `train` and parameters have been tuned on the validation split `validation`.
  
  Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m-sentiment/raw/main/metric.json)).
  
  - F1 (micro): 0.7133669814392706
  - F1 (macro): 0.7158353597305398
  - Accuracy: 0.7133669814392706
  
  ### Usage
  Install tweetnlp via pip.
  ```shell
  pip install tweetnlp
  ```
  Load the model in python.
  ```python
  import tweetnlp
  model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-2021-124m-sentiment", max_length=128)
  model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}')
  ```
  
  
  
  ### Reference 
  
   
  ```
  @inproceedings{camacho-collados-etal-2022-tweetnlp,
      title={{T}weet{NLP}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia},
      author={Camacho-Collados, Jose and Rezaee, Kiamehr and Riahi, Talayeh and Ushio, Asahi and Loureiro, Daniel and Antypas, Dimosthenis and Boisson, Joanne and Espinosa-Anke, Luis and Liu, Fangyu and Mart{'\i}nez-C{'a}mara, Eugenio and others},
      author = "Ushio, Asahi  and
        Camacho-Collados, Jose",
      booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
      month = nov,
      year = "2022",
      address = "Abu Dhabi, U.A.E.",
      publisher = "Association for Computational Linguistics",
  }
  ```
  