| --- |
| 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 |
|
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|
