Update README.md

README.md

@@ -34,166 +34,56 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
+Python '''
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Set the device (use GPU if available)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Load the model and tokenizer from Hugging Face
+tokenizer = T5Tokenizer.from_pretrained("Vijayendra/T5-base-ddg")
+model = T5ForConditionalGeneration.from_pretrained("Vijayendra/T5-base-ddg").to(device)
+
+# Define your prompts
+input_prompts = [
+    "I am having a bad day at work",
+    "What should I do about my stress?",
+    "How can I improve my productivity?",
+    "I'm feeling very anxious today",
+    "What is the best way to learn new skills?",
+    "How do I deal with failure?",
+    "What do you think about the future of technology?",
+    "I want to improve my communication skills",
+    "How can I stay motivated at work?",
+    "What is the meaning of life?"
+]
+
+# Generate responses
+
+generated_responses = {}
+for prompt in input_prompts:
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=400, truncation=True, padding="max_length").to(device)
+    
+    model.eval()
+    with torch.no_grad():
+        generated_ids = model.generate(
+            input_ids=inputs['input_ids'],
+            attention_mask=inputs['attention_mask'],
+            max_length=40,
+            num_beams=7,
+            repetition_penalty=2.5,
+            length_penalty=2.0,
+            early_stopping=True
+        )
+
+    # Decode the generated response
+    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    generated_responses[prompt] = generated_text
+
+# Display the input prompts and the generated responses
+
+for prompt, response in generated_responses.items():
+    print(f"Prompt: {prompt}")
+    print(f"Response: {response}\n")
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[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
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
-
-## Model Card Contact
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-[More Information Needed]