Update README.md

README.md

@@ -3,20 +3,60 @@ library_name: peft
 ---
 ## Training procedure
 
-Used PEFT library from huggingface and leveraged LoRA procedure to tune the model. Below are the training metrics. 
-
-Epoch	Training Loss	Validation Loss	Precision	Recall	F1	Accuracy
-1	0.392600	0.347941	0.762406	0.631506	0.690810	0.882263
-2	0.336300	0.302746	0.775583	0.702650	0.737317	0.897062
-3	0.309500	0.294454	0.817472	0.701828	0.755249	0.905303
-4	0.296700	0.281895	0.839335	0.695757	0.760831	0.905240
-5	0.281700	0.273324	0.816995	0.752103	0.783207	0.914322
-6	0.257300	0.262116	0.813662	0.758553	0.785142	0.915958
-7	0.241200	0.255580	0.819946	0.764308	0.791150	0.918980
-8	0.229900	0.255078	0.819697	0.771074	0.794643	0.919821
-9	0.212800	0.248312	0.830942	0.776450	0.802772	0.922594
-10	0.200900	0.245995	0.831402	0.780244	0.805011	0.923544
+- Used PEFT library from huggingface and leveraged LoRA procedure to tune the model. Below are the training metrics. 
 
+| Epoch | Training Loss | Validation Loss | Precision |   Recall |       F1 | Accuracy |
+|------:|--------------:|----------------:|----------:|---------:|---------:|---------:|
+|     1 |      0.392600 |        0.347941 |  0.762406 | 0.631506 | 0.690810 | 0.882263 |
+|     2 |      0.336300 |        0.302746 |  0.775583 | 0.702650 | 0.737317 | 0.897062 |
+|     3 |      0.309500 |        0.294454 |  0.817472 | 0.701828 | 0.755249 | 0.905303 |
+|     4 |      0.296700 |        0.281895 |  0.839335 | 0.695757 | 0.760831 | 0.905240 |
+|     5 |      0.281700 |        0.273324 |  0.816995 | 0.752103 | 0.783207 | 0.914322 |
+|     6 |      0.257300 |        0.262116 |  0.813662 | 0.758553 | 0.785142 | 0.915958 |
+|     7 |      0.241200 |        0.255580 |  0.819946 | 0.764308 | 0.791150 | 0.918980 |
+|     8 |      0.229900 |        0.255078 |  0.819697 | 0.771074 | 0.794643 | 0.919821 |
+|     9 |      0.212800 |        0.248312 |  0.830942 | 0.776450 | 0.802772 | 0.922594 |
+|    10 |      0.200900 |        0.245995 |  0.831402 | 0.780244 | 0.805011 | 0.923544 |
+
+- Model got shrunk by nearly 60 times and with the same efficiency as distilbert-base-uncased
+
+## Inference
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+from peft import get_peft_config, PeftModel, PeftConfig, get_peft_model, LoraConfig, TaskType
+
+peft_model_id = "vishnun/lora-NLIGraph"
+config = PeftConfig.from_pretrained(peft_model_id)
+inference_model = AutoModelForTokenClassification.from_pretrained(
+    config.base_model_name_or_path, num_labels=4, id2label=id2lab, label2id=lab2id
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(inference_model, peft_model_id)
+
+text = "Arsenal will win the Premier League"
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+tokens = inputs.tokens()
+predictions = torch.argmax(logits, dim=2)
+
+for token, prediction in zip(tokens, predictions[0].numpy()):
+    print((token, model.config.id2label[prediction]))
+
+## results : ('<s>', 'O')
+('Arsenal', 'SRC')
+('Ġwill', 'O')
+('Ġwin', 'REL')
+('Ġthe', 'O')
+('ĠPremier', 'TGT')
+('ĠLeague', 'O')
+('</s>', 'O')
+
+```
 ### Framework versions