Text Classification
Transformers
PyTorch
English
distilbert
classification
text-embeddings-inference
Instructions to use lingwave-admin/state-op-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lingwave-admin/state-op-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lingwave-admin/state-op-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lingwave-admin/state-op-detector") model = AutoModelForSequenceClassification.from_pretrained("lingwave-admin/state-op-detector") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -30,4 +30,7 @@ The core of the tool is a DistilBERT language transformer model that has been fi
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You can try out the model by entering in a sequence of 1-10 tweets. Each should be separated by pipes, as follows: "this is tweet one | this is tweet two." The model will then classify the sequence as belonging to a state operator or a normal user.
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## Further Information
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You can obtain further information on the data collection and training used to create this model at the following Github repo: [State Social Operator Detection](https://github.com/curt-tigges/state-social-operator-detection)
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You can try out the model by entering in a sequence of 1-10 tweets. Each should be separated by pipes, as follows: "this is tweet one | this is tweet two." The model will then classify the sequence as belonging to a state operator or a normal user.
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## Further Information
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You can obtain further information on the data collection and training used to create this model at the following Github repo: [State Social Operator Detection](https://github.com/curt-tigges/state-social-operator-detection)
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## Contact
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You can reach me at projects@curttigges.com.
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