Update README.md

README.md

@@ -1,39 +1,56 @@
 ---
+license: cc-by-sa-4.0
 library_name: transformers
-tags: []
+tags:
+- flan-t5
+- detoxification
+- polite-rewriting
+- text-to-text
+model_name: flan-paradetox-full
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
+# FLAN-T5-Large **Polite-Rewrite** (full fine-tune)
+
+**Model:** `google/flan-t5-large` fine-tuned for toxic → polite rewriting.
+
+## Training details
+| Parameter | Value |
+|-----------|-------|
+| epochs | 3 |
+| effective batch | 32 (16 × grad_acc=2, fp16) |
+| lr / schedule | 3 e-5, cosine, 3 % warm-up |
+| total steps | 1 800 |
+| optimizer | AdamW, weight_decay=0.01 |
+| hardware | 1 × A100-40 GB |
+
+### Data
+Merged **29 k** parallel pairs  
+* ParaDetox (19 k)  
+* Polite Insult (1.6 k, oversample×2)  
+* PseudoParaDetox Llama-3 (8.6 k, tox≤0.3, cosine≥0.8)
+
+### Metrics (dev 3 %)
+| metric | score |
+|--------|-------|
+| BLEU | 0.82 |
+| Avg toxicity (Detoxify) | **0.12** (src 0.71 → tgt 0.12) |
+| Success rate (tox≤0.5 AND -20 %) | 89 % |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tok = AutoTokenizer.from_pretrained("RinaldiDev/flan-paradetox-full")
+model = AutoModelForSeq2SeqLM.from_pretrained("RinaldiDev/flan-paradetox-full")
+
+def rewrite_polite(text):
+    inp = f"Rewrite politely:\\nInput: {text}\\nPolite:"
+    ids = tok(inp, return_tensors="pt").input_ids
+    out = model.generate(ids, num_beams=4, max_length=96)
+    return tok.decode(out[0], skip_special_tokens=True)
+
+print(rewrite_polite("Shut up, idiot!"))
+# → "Stop talking"
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
@@ -41,7 +58,10 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+
+AI moderation helper
+Toxic-to-polite assistants
+Not for hallucination-free tasks; may still miss subtle hate speech.
 
 ### Downstream Use [optional]
 
@@ -59,7 +79,9 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+
+Trained largely on English; fails on code-switching.
+Llama-generated pairs could contain artifacts.
 
 ### Recommendations