Text Classification
Transformers
Safetensors
Portuguese
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use Silly-Machine/TuPy-Bert-Base-Multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silly-Machine/TuPy-Bert-Base-Multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silly-Machine/TuPy-Bert-Base-Multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silly-Machine/TuPy-Bert-Base-Multilabel") model = AutoModelForSequenceClassification.from_pretrained("Silly-Machine/TuPy-Bert-Base-Multilabel") - Notebooks
- Google Colab
- Kaggle
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Parent(s): 51a4b2a
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README.md
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name: TuPyE-Dataset
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type: Silly-Machine/TuPyE-Dataset
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metrics:
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- type: accuracy
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value: 0.901
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name: Accuracy
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verified: true
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value: 0.
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name: F1-score
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verified: true
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value: 0.
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name: Precision
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verified: true
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- type: recall
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value: 0.
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name: Recall
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verified: true
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---
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name: TuPyE-Dataset
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type: Silly-Machine/TuPyE-Dataset
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metrics:
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- type: f1
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value: 0.85
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name: F1-score
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verified: true
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- type: precision
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value: 0.86
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name: Precision
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verified: true
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- type: recall
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value: 0.85
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name: Recall
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verified: true
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---
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