Update README.md

README.md

@@ -52,19 +52,25 @@ Model for predicting relations between entities in the financial documents.
 ```python
 finetune_name = 'Askinkaty/llama-finance-relations'
 
+
 finetined_model = AutoPeftModelForCausalLM.from_pretrained(
     pretrained_model_name_or_path=finetune_name,
     torch_dtype=torch.float16,
     low_cpu_mem_usage=True,
+    device_map="auto",
 )
 
+base_model_name = "meta-llama/Llama-3.2-1B-Instruct"
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name,
+                                                  torch_dtype=torch.float16,
+                                                  low_cpu_mem_usage=True)
 
-base_model = "meta-llama/Llama-3.2-1B-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-base_model.config.pad_token_id = base_model.config.eos_token_id
 
+base_model.config.pad_token_id = base_model.config.eos_token_id
+model.config.pad_token_id = model.config.eos_token_id
 
-pipeline = pipeline('text-generation', model=base_model, tokenizer=tokenizer)
+pipeline = pipeline('text-generation', model=base_model, tokenizer=tokenizer, max_length=1024, device=device)
 pipeline.model = model.to(device) 
 ```
 
@@ -141,18 +147,40 @@ LORA parameters:
 Test set sampled from Samples from [ReFinD dataset](https://refind-re.github.io/).
 
 
-
-
-
 #### Metrics
 
-Accuracy. Other metrics: work in progress.
-
 
+```
+Overall Performance:
+Precision: 0.77
+Recall: 0.69
+F1 Score: 0.71
+
+Classification Report:
+                  precision    recall  f1-score   support
+
+     no_relation       0.00      0.00      0.00         0
+           title       0.00      0.00      0.00         0
+   operations_in       0.65      0.66      0.66       100
+     employee_of       0.00      0.00      0.00         0
+  agreement_with       0.58      0.88      0.70       100
+       formed_on       0.00      0.00      0.00         0
+       member_of       0.99      0.96      0.97        96
+   subsidiary_of       0.00      0.00      0.00         0
+       shares_of       0.00      0.00      0.00         0
+      revenue_of       0.60      0.27      0.38        95
+         loss_of       0.64      0.37      0.47       100
+headquartered_in       0.99      0.73      0.84       100
+     acquired_on       0.00      0.00      0.00         0
+      founder_of       0.74      0.77      0.76        83
+       formed_in       0.96      0.91      0.93       100
+
+        accuracy                           0.69       774
+       macro avg       0.41      0.37      0.38       774
+    weighted avg       0.77      0.69      0.71       774
 
-### Results
+```
 
-Accuracy: 0.71
 
 
 - PEFT 0.14.0