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bert-base-multilingual-cased-sentiment-multilingual

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bert-base-multilingual-cased-sentiment-multilingual
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---
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"
}
```