README.md

---
datasets:
- cardiffnlp/tweet_sentiment_multilingual
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-xlm-roberta-base-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: 0.6931034482758621
    - name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
      type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
      value: 0.692628774202147
    - name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all)
      type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all
      value: 0.6931034482758621
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-xlm-roberta-base-sentiment-multilingual 

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) 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/twitter-xlm-roberta-base-sentiment-multilingual/raw/main/metric.json)).

- F1 (micro): 0.6931034482758621
- F1 (macro): 0.692628774202147
- Accuracy: 0.6931034482758621

### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-xlm-roberta-base-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{camacho-collados-etal-2022-tweetnlp,
    title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
    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,
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-demos.5",
    pages = "38--49"
}

```