Update README.md

README.md

@@ -6,6 +6,7 @@ tags:
 - generated_from_trainer
 datasets:
 - imagefolder
+- FastJobs/Visual_Emotional_Analysis
 model-index:
 - name: emotion_classification
   results: []
@@ -16,19 +17,69 @@ should probably proofread and complete it, then remove this comment. -->
 
 # emotion_classification
 
-This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the imagefolder dataset.
+This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
 
-## Model description
-
-More information needed
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
+### Intended Uses
+- Emotion classification from visual inputs (images).
+
+### Limitations
+- May reflect biases from the training dataset.
+- Performance may degrade in domains outside the training data.
+- Not suitable for critical or sensitive decision-making tasks.
+
+##  How to use this model
+```python
+from transformers import (PaliGemmaProcessor,PaliGemmaForConditionalGeneration,)
+from transformers.image_utils import load_image
+import torch
+from transformers import BitsAndBytesConfig
+from peft import get_peft_model
+from huggingface_hub import login
+from PIL import Image
+login(api_key)
+
+device = "cuda" if torch.cuda.is_available() else "CPU"
+
+bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_type=torch.bfloat16
+)
+
+# Load base model
+model_id = "google/paligemma-3b-pt-224"
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
+processor = PaliGemmaProcessor.from_pretrained(model_id)
+
+# Load adapter
+adapter_path = "digo-prayudha/emotion_classification" 
+model = PeftModel.from_pretrained(model, adapter_path)
+
+image = Image.open("image.jpg").convert("RGB")
+
+prompt = (
+    "Classify the emotion expressed in this image."
+)
+
+inputs = processor(
+    text=prompt,
+    images=image,
+    return_tensors="pt",
+    padding="longest",
+    tokenize_newline_separately=False
+).to(model.device)
+
+model.eval() 
+with torch.no_grad():
+    outputs = model.generate(**inputs)
+
+decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
+
+print("Predicted Emotion:", decoded_output)
+```
 
 ## Training procedure
 
@@ -47,7 +98,16 @@ The following hyperparameters were used during training:
 - num_epochs: 5
 
 ### Training results
-
+  | Step | Validation Loss |
+  |:----:|:---------------:|
+  | 100  | 2.684700        |
+  | 200  | 1.282700        |
+  | 300  | 1.085600        |
+  | 400  | 0.984500        |
+  | 500  | 0.861300        |
+  | 600  | 0.822900        |
+  | 700  | 0.807100        |
+  | 800  | 0.753300        |
 
 
 ### Framework versions