Instructions to use igoramf/lora-pt-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igoramf/lora-pt-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="igoramf/lora-pt-sentiment-analysis")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("igoramf/lora-pt-sentiment-analysis", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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AutoTokenizer,
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AutoConfig,
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AutoModelForSequenceClassification,
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DataCollatorWithPadding,
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TrainingArguments,
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Trainer
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)
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from peft import PeftModel, PeftConfig, get_peft_model
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model_repo = "igoramf/lora-pt-sentiment-analysis"
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AutoTokenizer,
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AutoConfig,
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AutoModelForSequenceClassification,
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)
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from peft import PeftModel, PeftConfig, get_peft_model
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model_repo = "igoramf/lora-pt-sentiment-analysis"
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