Update README.md

README.md

@@ -1,71 +1,86 @@
----
-base_model: distilbert-base-uncased
-library_name: peft
-license: apache-2.0
-metrics:
-- accuracy
-tags:
-- generated_from_trainer
-model-index:
-- name: distilbert-base-uncased-lora-text-classification
-  results: []
----
+# This is a custom dataset fine tune llm model using LoRA
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+### Run the code in Google Colab ---> Change Runtime to "T4 GPU" for faster training 
 
-# distilbert-base-uncased-lora-text-classification
+# DistilBERT-base-uncased LoRA Text Classification Model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4649
-- Accuracy: {'accuracy': 0.8416206261510129}
+## Model Description
+This model is a fine-tuned version of `distilbert-base-uncased` on an unspecified dataset. It achieves the following results on the evaluation set:
 
-## Model description
+- **Loss:** 0.4649
+- **Accuracy:** 84.16%
 
-More information needed
+## Intended Uses & Limitations
+This is a text-classification based model.
 
-## Intended uses & limitations
+## Training and Evaluation Data
+Look below for more details about the performances.
 
-More information needed
+## Steps to follow
+- Installing the Libraries
+- Loading the Dataset from HuggingFace
+- Train_test Split the Dataset
+- Model
+- Preprocess Data
+- Evaluation
+- Apply untrained base model("distilbert-base-uncased") to text
+- Train Model using LoRA
+- Generate Prediction
+- Save the Model and the Tokenizer
+- Load the Model and the Tokenizer to test
+- Push Model to HuggingFaceHub
 
-## Training and evaluation data
 
-More information needed
 
-## Training procedure
-
-### Training hyperparameters
 
+### Training Hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 0.001
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 10
-
-### Training results
-
-| Training Loss | Epoch | Step   | Validation Loss | Accuracy                         |
-|:-------------:|:-----:|:------:|:---------------:|:--------------------------------:|
-| 0.5924        | 1.0   | 10744  | 0.5523          | {'accuracy': 0.7845303867403315} |
-| 0.5983        | 2.0   | 21488  | 0.5236          | {'accuracy': 0.8029465930018416} |
-| 0.5703        | 3.0   | 32232  | 0.4498          | {'accuracy': 0.7955801104972375} |
-| 0.5526        | 4.0   | 42976  | 0.4976          | {'accuracy': 0.8066298342541437} |
-| 0.5326        | 5.0   | 53720  | 0.4317          | {'accuracy': 0.8084714548802947} |
-| 0.5851        | 6.0   | 64464  | 0.4562          | {'accuracy': 0.8287292817679558} |
-| 0.5466        | 7.0   | 75208  | 0.4713          | {'accuracy': 0.8195211786372008} |
-| 0.5494        | 8.0   | 85952  | 0.5072          | {'accuracy': 0.8250460405156538} |
-| 0.5748        | 9.0   | 96696  | 0.4802          | {'accuracy': 0.8287292817679558} |
-| 0.5001        | 10.0  | 107440 | 0.4649          | {'accuracy': 0.8416206261510129} |
-
-
-### Framework versions
-
-- PEFT 0.12.0
-- Transformers 4.42.4
-- Pytorch 2.4.0+cu121
-- Datasets 2.21.0
-- Tokenizers 0.19.1
+
+- **Learning Rate:** 0.001
+- **Train Batch Size:** 4
+- **Eval Batch Size:** 4
+- **Seed:** 42
+- **Optimizer:** Adam with betas=(0.9,0.999) and epsilon=1e-08
+- **LR Scheduler Type:** Linear
+- **Number of Epochs:** 10
+
+### Training Results
+| Epoch | Training Loss | Validation Loss | Validation Accuracy |
+|-------|---------------|-----------------|---------------------|
+| 1.0   | 0.5924        | 0.5523          | 78.45%              |
+| 2.0   | 0.5983        | 0.5236          | 80.29%              |
+| 3.0   | 0.5703        | 0.4498          | 79.56%              |
+| 4.0   | 0.5526        | 0.4976          | 80.66%              |
+| 5.0   | 0.5326        | 0.4317          | 80.85%              |
+| 6.0   | 0.5851        | 0.4562          | 82.87%              |
+| 7.0   | 0.5466        | 0.4713          | 81.95%              |
+| 8.0   | 0.5494        | 0.5072          | 82.50%              |
+| 9.0   | 0.5748        | 0.4802          | 82.87%              |
+| 10.0  | 0.5001        | 0.4649          | 84.16%              |
+
+## Framework Versions
+- **PEFT:** 0.12.0
+- **Transformers:** 4.42.4
+- **PyTorch:** 2.4.0+cu121
+- **Datasets:** 2.21.0
+- **Tokenizers:** 0.19.1
+
+# Dataset Viewer
+
+You can view the dataset using the following link:
+
+[View Twitter Sentiment Preprocessed Dataset](https://huggingface.co/datasets/shukdevdatta123/twitter_sentiment_preprocessed/)
+
+Simply click the link to open the dataset viewer in your browser.
+
+# Model Viewer
+
+You can view the model using the following link:
+
+[View Model in HuggingFace](https://huggingface.co/shukdevdatta123/distilbert-base-uncased-lora-text-classification/)
+
+Simply click the link to open the model file in your browser.
+
+Check out the "Fine-tune LLM.pptx" file for the theory behind this code.
+
+