readme: ---
  datasets:
  - cardiffnlp/tweet_sentiment_multilingual
  metrics:
  - f1
  - accuracy
  model-index:
  - name: cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: cardiffnlp/tweet_sentiment_multilingual
        type: all
        split: test 
      metrics:
      - name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
        type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
        value: 0.6169540229885058
      - name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
        type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
        value: 0.6168385894019698
      - name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all)
        type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all
        value: 0.6169540229885058
  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/bert-base-multilingual-cased-sentiment-multilingual 
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the 
  [`cardiffnlp/tweet_sentiment_multilingual (all)`](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) 
  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/bert-base-multilingual-cased-sentiment-multilingual/raw/main/metric.json)).
  
  - F1 (micro): 0.6169540229885058
  - F1 (macro): 0.6168385894019698
  - Accuracy: 0.6169540229885058
  
  ### Usage
  Install tweetnlp via pip.
  ```shell
  pip install tweetnlp
  ```
  Load the model in python.
  ```python
  import tweetnlp
  model = tweetnlp.Classifier("cardiffnlp/bert-base-multilingual-cased-sentiment-multilingual", 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{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"
  }
  ```
  
  
  