---
license: mit
tags:
- generated_from_trainer
datasets:
- tweet_eval
model-index:
- name: selims
  results: []
widget:
- text: "I love conducting research on twins!"
  example_title: "Sentiment analysis - English"
- text: "Ja, ik vind het tweelingen onderzoek leuk maar complex, weet je."
  example_title: "Sentiment analysis - Dutch"
---

# selims

This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the tweet_eval dataset.

## Model description

This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews.

## Intended uses & limitations

This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. 
## Training and evaluation data
For fine-tunning of this model, the Tweet_eval dataset was used.

## Training procedure
Please refer to the information below:

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3