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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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+ - f1
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+ - recall
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+ - precision
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # FLAN-T5 small-GeoNames
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+ This model is a fine-tuned version of [flan-t5-small](https://huggingface.co/google/flan-t5-small) on the GeoNames dataset.
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+
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+ ## Model description
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+
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+ The model is trained to classify terms into one of 660 category classes related to geographical locations.
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+ 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).
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+
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+ ## Intended uses and limitations
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+ This model is intended to be used to generate a type (class) for an input term.
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+
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+ # Training and evaluation data
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+ 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).
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+ The train size is 8078865.
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+ The test size is 702510.
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+ ## Example
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+ Here's an example of the model capabilities:
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+
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+ - **input:**
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+ - *Lexical Term L:* Pic de Font Blanca
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+
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+ - **output:**
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+ - *Type:* peak
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+
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+ - **input:**
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+ - *Lexical Term L:* Roc Mele
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+
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+ - **output:**
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+ - *Type:* mountain
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+
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+ - **input:**
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+ - *Lexical Term L:* Estany de les Abelletes
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+
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+ - **output:**
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+ - *Type:* lake
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - num_epochs: 5
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+
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+ ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 2.6223 | 1.0 | 1000 | 1.5223 |
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+ | 2.1430 | 2.0 | 2000 | 1.3764 |
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+ | 1.9100 | 3.0 | 3000 | 1.2825 |
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+ | 1.7642 | 4.0 | 4000 | 1.2102 |
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+ | 1.6607 | 5.0 | 5000 | 1.1488 |