Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use marieke93/MiniLM-evidence-types with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marieke93/MiniLM-evidence-types with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marieke93/MiniLM-evidence-types")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marieke93/MiniLM-evidence-types") model = AutoModelForSequenceClassification.from_pretrained("marieke93/MiniLM-evidence-types") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- Accuracy: 0.7161
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- Balanced accuracy: 0.3616
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## Model description
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This model will give a senctence one of eight labels:
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- assumption
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- anecdote
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- testimony
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- definition
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- statistics/study
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- other
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- none
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- continue
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## Training and evaluation data
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The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
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- Accuracy: 0.7161
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- Balanced accuracy: 0.3616
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## Training and evaluation data
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The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
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