---
license: mit
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
library_name: transformers
pipeline_tag: text-generation
---

# FLAN-T5 small-GeoNames

This model is a fine-tuned version of [flan-t5-small](https://huggingface.co/google/flan-t5-small) on the GeoNames dataset.

## Model description

The model is trained to classify terms into one of 660 category classes related to geographical locations.

The model also works well as part of a Retrieval-and-Generation (RAG) pipeline by leveraging an external knowledge source, specifically [GeoNames Semantic Primes](https://huggingface.co/datasets/HannaAbiAkl/geonames-semantic-primes).

## Intended uses and limitations

This model is intended to be used to generate a type (class) for an input term.

# Training and evaluation data

The training and evaluation data can be found [here](https://github.com/HamedBabaei/LLMs4OL-Challenge-ISWC2024/tree/main/TaskA-Term%20Typing/SubTask%20A.2%20(FS)%20-%20GeoNames).

The train size is 8078865.

The test size is 702510.

## Example

Here's an example of the model capabilities:

- **input:**
  - *Lexical Term L:* Pic de Font Blanca

- **output:**
  - *Type:* peak

- **input:**
  - *Lexical Term L:* Roc Mele

- **output:**
  - *Type:* mountain

- **input:**
  - *Lexical Term L:* Estany de les Abelletes

- **output:**
  - *Type:* lake
 
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6223        | 1.0   | 1000 | 1.5223          |
| 2.1430	    | 2.0   | 2000 | 1.3764          |
| 1.9100	    | 3.0   | 3000 | 1.2825          |
| 1.7642	    | 4.0   | 4000 | 1.2102          |
| 1.6607        | 5.0   | 5000 | 1.1488          |