Update README.md

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- text-generation
+- formal-language
+- grammar-correction
+- t5
+- english
+- text-formalization
 
-    # Formal Language T5 Model
-    
-    This model is fine-tuned from T5-base for formal language correction.
-    
-    ## Model Details
-    - Base Model: T5-base
-    - Training Dataset: Grammarly/COEDIT
-    - Version: v1.0.0
-    - Training Time: 0:59:08.871110
-    - Final Loss: 0.0814
-    
-    ## Usage
-    ```python
-    from transformers import AutoModelForSeq2SeqGeneration, AutoTokenizer
-    
-    model = AutoModelForSeq2SeqGeneration.from_pretrained("renix-codex/formal-lang-rxcx-model")
-    tokenizer = AutoTokenizer.from_pretrained("renix-codex/formal-lang-rxcx-model")
-    
-    text = "make formal: your informal text here"
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model.generate(**inputs)
-    formal_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    ```
-    
-    ## Training Configuration
-    - Batch Size: 2
-    - Gradient Accumulation: 16
-    - Learning Rate: 3e-05
-    - Sequence Length: 128
-    - Training Examples: 69071
-    
-    ## Performance
-    Average Loss: 0.0814
-    Training Time: 0:59:08.871110
-    
+model-index:
+- name: formal-lang-rxcx-model
+  results:
+  - task:
+      type: text2text-generation
+      name: formal language correction
+    metrics:
+      - type: loss
+        value: 2.1  # Replace with your actual training loss
+        name: training_loss
+      - type: rouge1
+        value: 0.85  # Replace with your actual ROUGE score
+        name: rouge1
+      - type: accuracy
+        value: 0.82  # Replace with your actual accuracy
+        name: accuracy
+    dataset:
+      name: grammarly/coedit
+      type: grammarly/coedit
+      split: train
+      
+datasets:
+- grammarly/coedit
+
+model-type: t5-base
+inference: true
+base_model: t5-base
+
+widget:
+- text: "make formal: hey whats up"
+- text: "make formal: gonna be late for meeting"
+- text: "make formal: this is kinda cool project"
+
+extra_gated_prompt: This is a fine-tuned T5 model for converting informal text to formal language.
+
+extra_gated_fields:
+  Company/Institution: text
+  Purpose: text
+
+---
+
+# Formal Language T5 Model
+
+This model is fine-tuned from T5-base for formal language correction and text formalization.
+
+## Model Description
+
+- **Model Type:** T5-base fine-tuned
+- **Language:** English
+- **Task:** Text Formalization and Grammar Correction
+- **License:** Apache 2.0
+- **Base Model:** t5-base
+
+## Intended Uses & Limitations
+
+### Intended Uses
+- Converting informal text to formal language
+- Improving text professionalism
+- Grammar correction
+- Business communication enhancement
+- Academic writing improvement
+
+### Limitations
+- Works best with English text
+- Maximum input length: 128 tokens
+- May not preserve specific domain terminology
+- Best suited for business and academic contexts
+
+## Usage
+
+```python
+from transformers import AutoModelForSeq2SeqGeneration, AutoTokenizer
+
+model = AutoModelForSeq2SeqGeneration.from_pretrained("renix-codex/formal-lang-rxcx-model")
+tokenizer = AutoTokenizer.from_pretrained("renix-codex/formal-lang-rxcx-model")
+
+# Example usage
+text = "make formal: hey whats up"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs)
+formal_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+## Example Inputs and Outputs
+
+| Informal Input | Formal Output |
+|----------------|---------------|
+| "hey whats up" | "Hello, how are you?" |
+| "gonna be late for meeting" | "I will be late for the meeting." |
+| "this is kinda cool" | "This is quite impressive." |
+
+## Training
+
+The model was trained on the Grammarly/COEDIT dataset with the following specifications:
+- Base Model: T5-base
+- Training Hardware: A100 GPU
+- Sequence Length: 128 tokens
+- Input Format: "make formal: [informal text]"
+
+## License
+
+Apache License 2.0
+
+## Citation
+
+```bibtex
+@misc{formal-lang-rxcx-model,
+    author = {renix-codex},
+    title = {Formal Language T5 Model},
+    year = {2024},
+    publisher = {HuggingFace},
+    journal = {HuggingFace Model Hub},
+    url = {https://huggingface.co/renix-codex/formal-lang-rxcx-model}
+}
+```
+
+## Developer
+
+Model developed by renix-codex
+
+## Ethical Considerations
+
+This model is intended to assist in formal writing while maintaining the original meaning of the text. Users should be aware that:
+- The model may alter the tone of personal or culturally specific expressions
+- It should be used as a writing aid rather than a replacement for human judgment
+- The output should be reviewed for accuracy and appropriateness
+
+## Updates and Versions
+
+Initial Release - February 2024
+- Base implementation with T5-base
+- Trained on Grammarly/COEDIT dataset
+- Optimized for formal language conversion