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---
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 |
```
@misc{akl2024dstillms4ol2024task,
title={DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification},
author={Hanna Abi Akl},
year={2024},
eprint={2408.14236},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.14236},
}
``` |