Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Mudasir692
+- **Model type:** transformer
+- **Language(s) (NLP):** python
+- **License:** MIT
+- **Finetuned from model [optional]:** Peguses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+
+## Bias, Risks, and Limitations
+
+Model might not generate coherent summary to large extent.
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+import torch
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+
+# Load the saved model and tokenizer
+model_path = "peguses_chat_sum"
+device = torch.device("cpu")
+
+# Load the model and tokenizer from the saved directory
+model = PegasusForConditionalGeneration.from_pretrained(model_path)
+tokenizer = PegasusTokenizer.from_pretrained(model_path)
+
+# Move the model to the correct device
+model = model.to(device)
+
+
+# Define the inference function
+def inference(input_text):
+    # Tokenize input text
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    input_ids = inputs["input_ids"].to(device)
+    attention_mask = inputs["attention_mask"].to(device)
+
+    # Generate summary
+    model.eval()
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=150,
+            num_beams=8,
+            early_stopping=True,
+        )
+
+    # Decode the generated text
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+
+# Test with a sample input
+test_input = """
+#Person1#: Hey Alice, congratulations on your promotion!
+#Person2#: Thank you so much! It means a lot to me. I’m still processing it, honestly.
+#Person1#: You totally deserve it. Your hard work finally paid off. Let’s celebrate this weekend.
+#Person2#: That sounds amazing. Dinner on me, okay?
+#Person1#: Sure! Just let me know where and when. Oh, by the way, did you tell your family?
+#Person2#: Yes, they were so excited. Mom’s already planning to bake a cake.
+#Person1#: That’s wonderful! I’ll bring a gift too. It’s such a big milestone for you.
+#Person2#: You’re the best. Thanks for always being so supportive.
+"""
+print("Summary Text:", inference(test_input))
+
+