Update README.md

README.md

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----
-license: mit
----
+---
+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          |