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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tweet_eval |
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widget: |
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- text: I love conducting research on twins! |
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example_title: Sentiment analysis - English |
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- text: Ja, ik vind het tweelingen onderzoek leuk maar complex, weet je. |
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example_title: Sentiment analysis - Dutch |
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base_model: nlptown/bert-base-multilingual-uncased-sentiment |
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model-index: |
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- name: selims |
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results: [] |
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--- |
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# selims |
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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. |
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## Model description |
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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. |
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## Intended uses & limitations |
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This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. |
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## Training and evaluation data |
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For fine-tunning of this model, the Tweet_eval dataset was used. |
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## Training procedure |
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Please refer to the information below: |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.1+cpu |
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- Datasets 2.0.0 |
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- Tokenizers 0.10.3 |
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