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---
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 | |