README.md

---
library_name: peft
license: mit
datasets:
- vishnun/NLP-KnowledgeGraph
language:
- en
metrics:
- accuracy
pipeline_tag: token-classification
tags:
- nlp
- deep learning
- code
---
## 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 |

- 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


- PEFT 0.4.0