Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Cross-lingual-nlp
zero-shot-transfer
toxicity-analysis
abuse-detection
flag-user
block-user
multilinguality
XLM-R
Instructions to use Jayveersinh-Raj/PolyGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayveersinh-Raj/PolyGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jayveersinh-Raj/PolyGuard")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jayveersinh-Raj/PolyGuard") model = AutoModelForSequenceClassification.from_pretrained("Jayveersinh-Raj/PolyGuard") - Notebooks
- Google Colab
- Kaggle
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- **Language(s) (NLP):** Pytorch, ONNX
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- **License:** apache-2.0
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/Jayveersinh-Raj/cross-lingual-zero-shot-transfer
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- **Paper
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- **Demo
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## Uses
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### Downstream Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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The model fine tuning is not needed the model already performs well. However can be fine tuned to add languages that are written with different scripts since our model does not perform on language with different script then the source.
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- **Language(s) (NLP):** Pytorch, ONNX
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- **License:** apache-2.0
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/Jayveersinh-Raj/cross-lingual-zero-shot-transfer
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- **Paper:** Everything is in the above github repository Make sure to give it a star if it is useful.
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- **Demo:**
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## Uses
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### Downstream Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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The model fine tuning is not needed the model already performs well. However can be fine tuned to add languages that are written with different scripts since our model does not perform on language with different script then the source.
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