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

# FLAN-T5 small-WordNet

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

## Model description

The model is trained to classify terms into one of four term types: noun, verb, adjective or adverb. The types themselves are learned and then generated by the model with no more than one type associated with a specific term.

The model also works well as part of a Retrieval-and-Generation (RAG) pipeline by leveraging an external knowledge source, specifically [Wordnet Semantic Primes](https://huggingface.co./datasets/HannaAbiAkl/wordnet-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.1(FS)%20-%20WordNet).

The train size is 40559.

The test size is 9470.

## Example

Here's an example of the model capabilities:

- **input:**
  - *Lexical Term L:* question
  - *Sentence Containing L (Optional):* there was a question about my training

- **output:**
  - *Type:* noun
 
- **input:**
  - *Lexical Term L:* lodge
  - *Sentence Containing L (Optional):* Where are you lodging in Paris?

- **output:**
  - *Type:* verb
 
- **input:**
  - *Lexical Term L:* genus equisetum
  - *Sentence Containing L (Optional):* 

- **output:**
  - *Type:* noun
 
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1725        | 1.0   | 1000 | 0.0640          |
| 0.1250	    | 2.0   | 2000 | 0.0535          |
| 0.1040	    | 3.0   | 3000 | 0.0469          |
| 0.0917	    | 4.0   | 4000 | 0.0421          |
| 0.0830        | 5.0   | 5000 | 0.0384          |