Update README.md

README.md

@@ -61,21 +61,16 @@ Use the code below to get started with the model.
 In the following demonstration series, we show examples of matching and non-matching text predictions of our model for the key point "Social media regulation harms privacy" of the debatable topic "Social media platforms should be regulated by the government" and the focus is on arguments contesting the topic.
 ```
 import torch
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from peft import PeftModel, PeftConfig
 huggingface-cli login
 
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16)
-
 
+base_model_id="ilsp/Meltemi-7B-v1"
 peft_model_id = "Kleo/meltemi_arg2kp_matcher"
 peft_config = PeftConfig.from_pretrained(peft_model_id)
-model = AutoModelForSequenceClassification.from_pretrained(peft_config.base_model_name_or_path, return_dict=True, quantization_config=bnb_config, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
+model = AutoModelForSequenceClassification.from_pretrained(base_model_id,"device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
 
 # Load the Lora model
 model = PeftModel.from_pretrained(model, peft_model_id)
@@ -126,7 +121,7 @@ for text in matching_texts:
     results.append(predicted_class_id)
 
 # Print the results
-for idx, (text, label_id) in enumerate(zip(texts, results), start=1):
+for idx, (text, label_id) in enumerate(zip(matching_texts, results), start=1):
     print(f"Text {idx}: Predicted Label ID: {label_id}")
 ```