Update README.md

README.md

@@ -73,13 +73,13 @@ bnb_config = BitsAndBytesConfig(
     bnb_4bit_compute_dtype=torch.bfloat16)
 
 
-peft_model_id = "Kleo/meltemi_base_finetuning_kpm_kp_arg"
-config = PeftConfig.from_pretrained(peft_model_id)
-model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path, return_dict=True, quantization_config=bnb_config, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+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)
 
 # Load the Lora model
-model = PeftModel.from_pretrained(model, peft_model_id)
+model = PeftModel.from_pretrained(model, peft_config)
 
 tokenizer.pad_token_id = tokenizer.eos_token_id
 tokenizer.pad_token = tokenizer.eos_token
@@ -232,7 +232,10 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 - **Hardware Type:** Tesla P100
 - **Hours used:** 21 hours
-- **Cloud Provider:** Kaggle
+- **GPU memory**: 16GB
+- **Number of nodes**: 1
+- **Number of GPUs per node**: 1
+- **Cloud Provider:** [Kaggle](https://www.kaggle.com/)
 - **Carbon Emitted:** 0.1
 
 ## Technical Specifications
@@ -244,13 +247,6 @@ library, meaning it outputs two class labels, out of which the matching score is
 extracted. We consider as scores the probabilities of the class_1, meaning the 
 probability of a key point to match the argument.
 
-### Compute Infrastructure
-The model has been trained with the available [Kaggle](https://www.kaggle.com/) resources:
-- Number of nodes: 1
-- Number of GPUs per node: 1
-- GPU type: Tesla P100
-- GPU memory: 16GB
-
 
 ## Citation