Update README.md

README.md

@@ -11,4 +11,58 @@ license: mit
 short_description: Classifies each token in the input text as LOC, ORG, PER, or
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Flan-T5 Token Classifier (NER Demo)
+
+This Huggingface Space is a Gradio demo for the model [`pepegiallo/flan-t5-base_ner`](https://huggingface.co/pepegiallo/flan-t5-base_ner). It performs **token-level Named Entity Recognition (NER)** using a Flan-T5 encoder-based architecture.
+
+---
+
+## 🔍 What does this demo do?
+
+You can enter any sentence, and the app will:
+
+1. Split the sentence into tokens (words and punctuation)
+2. For each token:
+    - Mark it with `<TSTART>` and `<TEND>` in the context of the sentence
+    - Send it through the model with the prompt: `classify token in: <wrapped sentence>`
+3. Predict one of the following labels for each token:
+    - `PER` — Person
+    - `ORG` — Organization
+    - `LOC` — Location
+    - `O` — Not an entity
+
+---
+
+## 🧠 Example
+
+Input:
+```
+Max Mustermann works at Microsoft and lives in Berlin.
+```
+
+Output:
+```
+Max         -> PER
+Mustermann  -> PER
+Microsoft   -> ORG
+Berlin      -> LOC
+```
+
+---
+
+## 📦 Model Details
+
+- **Base model:** `google/flan-t5-base` (encoder only)
+- **Fine-tuned on:** WikiANN, open-pii-masking-500k, and custom samples
+- **Prompt-based classification** per token
+- **Architecture:** T5 encoder + classification head
+
+---
+
+## 🚀 Try it out!
+
+Type any sentence in English, German, French, Italian or Spanish, and the model will tag names, organizations, and locations.
+
+For more details, check the full model card:
+👉 [`pepegiallo/flan-t5-base_ner`](https://huggingface.co/pepegiallo/flan-t5-base_ner)
+