---
library_name: transformers
datasets:
- unimelb-nlp/wikiann
- ai4privacy/open-pii-masking-500k-ai4privacy
- custom
language:
- en
- de
- es
- it
- fr
base_model:
- google/flan-t5-base
---

# Model Card for Flan-T5 Base Token Classifier (NER: LOC, ORG, PER)

This model is a fine-tuned encoder-only version of `google/flan-t5-base` for **token-level Named Entity Recognition (NER)**. It predicts entity labels (e.g., LOC, ORG, PER) by classifying **individual tokens** in a prompting setup using `<TSTART>` and `<TEND>` markers.

---

## Model Details

### Model Description

This model is based on the encoder of the T5 architecture and has been fine-tuned for single-token classification using a prompt-driven approach. Given a sentence, one token is wrapped with `<TSTART>` and `<TEND>`, and the model predicts the corresponding entity class.

- **Developed by:** pepegiallo
- **Model type:** Encoder-only token classifier
- **Language(s) (NLP):** en, de, fr, it, es
- **License:** MIT
- **Finetuned from model:** google/flan-t5-base

## Uses

### Direct Use

You can use this model to classify named entities (PER, ORG, LOC, or O) one token at a time. This approach is suitable for tasks such as:

- PII detection
- Privacy-preserving document redaction
- Legal or medical text anonymization

### Out-of-Scope Use

- Full-sequence tagging (this model is optimized for classifying one token at a time)
- Multi-token entity recognition without aggregation logic

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and model
model = AutoModelForSequenceClassification.from_pretrained("pepegiallo/flan-t5-base_ner")
tokenizer = AutoTokenizer.from_pretrained("pepegiallo/flan-t5-base_ner")

# Helper: wrap a token with <TSTART> and <TEND>
def wrap_token(text, target_token, tstart="<TSTART>", tend="<TEND>"):
    return text.replace(target_token, f"{tstart} {target_token} {tend}")

text = "The headquarters of Microsoft is in Redmond."
target_token = "Microsoft"
prompt = "classify token in: " + wrap_token(text, target_token)

inputs = tokenizer(prompt, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
outputs = model(**inputs)
label_id = torch.argmax(outputs.logits, dim=-1).item()

id2label = {0: "LOC", 1: "ORG", 2: "PER", 3: "O"}
print("Predicted entity:", id2label[label_id])
```

---

## Training Details

The model was fine-tuned for 3 epochs on a multilingual, balanced dataset combining:

- `wikiann` (unimelb-nlp)
- `open-pii-masking-500k-ai4privacy`
- Custom annotated examples with `<TSTART>` / `<TEND>` tags

### Training Hyperparameters

- Model: google/flan-t5-base (encoder only)
- Batch size: 128
- Max input length: 128
- Optimizer: AdamW
- Learning rate: 3e-5
- Epochs: 3

## Evaluation

### Metrics

The model was evaluated using the following metrics:

- Accuracy
- Precision (Macro)
- Recall (Macro)
- F1 Score (Macro)

### Results

| Epoch | Training Loss | Validation Loss | Accuracy | Precision (Macro) | Recall (Macro) | F1 (Macro) |
|-------|---------------|-----------------|----------|-------------------|----------------|------------|
| 1     | 0.1702        | 0.1504          | 95.21%   | 0.9521            | 0.9521         | 0.9521     |
| 2     | 0.1444        | 0.1310          | 95.89%   | 0.9588            | 0.9589         | 0.9589     |
| 3     | 0.1290        | 0.1246          | 96.14%   | 0.9614            | 0.9614         | 0.9614     |

---

## Environmental Impact

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]

## Technical Specifications

- **Architecture:** T5 Encoder + Dense Classification Head
- **Precision:** fp32
- **Framework:** PyTorch + Huggingface Transformers

## Citation [optional]

```bibtex
@misc{flan-t5-ner,
  title={Token Classification with Flan-T5 Encoder},
  author={pepegiallo},
  year={2025},
  howpublished={\url{https://huggingface.co/pepegiallo/flan-t5-base_ner}}
}
```

## Model Card Contact

For questions, contact: [https://huggingface.co/pepegiallo](https://huggingface.co/pepegiallo)