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---
datasets:
- cardiffnlp/tweet_sentiment_multilingual
metrics:
- f1
- accuracy
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
base_model: bert-base-multilingual-cased
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:
- type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
value: 0.6169540229885058
name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
- type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
value: 0.6168385894019698
name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
- type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all
value: 0.6169540229885058
name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all)
---
# 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"
}
```
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