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---
title: Flan T5 Token Ner
emoji: 📚
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 5.23.3
app_file: app.py
pinned: false
license: mit
short_description: Classifies each token in the input text as LOC, ORG, PER, or
---
# 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)
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