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--- |
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library_name: peft |
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base_model: t5-base |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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Model Based in Amazon music instruments dataset |
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Reccomendation by prompt task |
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# T5-recs |
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T5 with prompt Tuning PEFT finetuned on Amazon purchased data |
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Model source: https://huggingface.co/Fidlobabovic/T5-recs |
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<head>Usage</head> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import get_peft_config, get_peft_model, LoraConfig, TaskType |
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model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") |
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model = PeftModel.from_pretrained(model, "Fidlobabovic/T5-recs") |
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tokenizer = AutoTokenizer.from_pretrained("Fidlobabovic/T5-recs") |
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input_text = "Purchases: { Guitar, Stradivary notes, synthesizer} Candidates: {Violin; Thrombon; Refrigirator; Sex toy;} - RECCOMENDATION :" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=5) |
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print("input user hitory: ", input_text) ## output rec: ['Guitar'] |
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print(" output rec: ", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)) |
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``` |
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