Text Classification
Transformers
Safetensors
Portuguese
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use Silly-Machine/TuPy-Bert-Large-Multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silly-Machine/TuPy-Bert-Large-Multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silly-Machine/TuPy-Bert-Large-Multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silly-Machine/TuPy-Bert-Large-Multilabel") model = AutoModelForSequenceClassification.from_pretrained("Silly-Machine/TuPy-Bert-Large-Multilabel") - Notebooks
- Google Colab
- Kaggle
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name: Silly-Machine/TuPy-Dataset
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type: Silly-Machine/TuPy-Dataset
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metrics:
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- name:
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type:
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value: 64.59
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source:
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name: Open LLM Leaderboard
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name: Silly-Machine/TuPy-Dataset
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type: Silly-Machine/TuPy-Dataset
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metrics:
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- name: f1
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type: f1
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value: 64.59
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source:
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name: Open LLM Leaderboard
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