Instructions to use CIS5190ml/bert4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIS5190ml/bert4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIS5190ml/bert4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIS5190ml/bert4") model = AutoModelForSequenceClassification.from_pretrained("CIS5190ml/bert4") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Model Information
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The model being evaluated is hosted under the Hugging Face Hub namespace `CIS5190ml/
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## Evaluation Pipeline
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from torch.utils.data import DataLoader
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("CIS5190ml/
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model = AutoModelForSequenceClassification.from_pretrained("CIS5190ml/
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# Load dataset
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ds = load_dataset("CIS5190ml/test_20_rows", split="train")
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## Model Information
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The model being evaluated is hosted under the Hugging Face Hub namespace `CIS5190ml/bert4`.
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## Evaluation Pipeline
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from torch.utils.data import DataLoader
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("CIS5190ml/bert4")
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model = AutoModelForSequenceClassification.from_pretrained("CIS5190ml/bert4")
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# Load dataset
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ds = load_dataset("CIS5190ml/test_20_rows", split="train")
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