Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use cartesinus/fedcsis-intent_baseline-xlm_r-pl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cartesinus/fedcsis-intent_baseline-xlm_r-pl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cartesinus/fedcsis-intent_baseline-xlm_r-pl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/fedcsis-intent_baseline-xlm_r-pl") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/fedcsis-intent_baseline-xlm_r-pl") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model (#1)
Browse files- Librarian Bot: Add base_model information to model (5576d34623d3d019565dc71b79881d6e114c7089)
Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>
README.md
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metrics:
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- accuracy
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- f1
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model-index:
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- name: fedcsis-intent_baseline-xlm_r-pl
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results: []
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metrics:
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- accuracy
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- f1
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base_model: xlm-roberta-base
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model-index:
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- name: fedcsis-intent_baseline-xlm_r-pl
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results: []
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