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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selimsametoglu/selims") model = AutoModelForSequenceClassification.from_pretrained("selimsametoglu/selims") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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README.md
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- generated_from_trainer
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datasets:
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- tweet_eval
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model-index:
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- name: selims
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results: []
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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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---
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# selims
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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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