Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use selimsametoglu/selims with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selimsametoglu/selims with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selimsametoglu/selims", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selimsametoglu/selims") model = AutoModelForSequenceClassification.from_pretrained("selimsametoglu/selims", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): f60deb2
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README.md
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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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