Update README.md

README.md

@@ -6,6 +6,16 @@ tags:
 model-index:
 - name: t5-small-squad-qg-v2
   results: []
+datasets:
+- rajpurkar/squad
+language:
+- en
+metrics:
+- bleu
+- rouge
+- meteor
+- bertscore
+pipeline_tag: text2text-generation
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,17 +23,101 @@ should probably proofread and complete it, then remove this comment. -->
 
 # t5-small-squad-qg-v2
 
-This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the SQuAD dataset.
 It achieves the following results on the evaluation set:
 - Loss: 1.6608
+- BLEU: 20.00
+- Rouge1: 47.69
+- Rouge2: 26.43
+- RougeL: 44.15
+- RougeLSum: 44.15
+- METEOR: 45.84
+- BertScore: 91.82
 
 ## Model description
 
-More information needed
 
 ## Intended uses & limitations
-
-More information needed
+1. Define some useful functions for highlighting the answer in the paragraph and preparing the instruction prompt that will be fed to the model:
+
+```Python
+def highlight_answer(context, answer):
+    context_splits = context.split(answer)
+    
+    text = ""
+    for split in context_splits:
+        text += split
+        text += ' <h> '
+        text += answer
+        text += ' <h> '
+        text += split
+    
+    return text
+
+def prepare_instruction(answer_highlighted_context):
+    instruction_prompt = f"""Generate a question whose answer is highlighted by <h> from the context delimited by the triple backticks.
+    context:
+    ```
+    {answer_highlighted_context}
+    ```
+    """
+    
+    return instruction_prompt
+```
+
+2. Use the model as a Hugging Face Pipeline:
+
+```Python
+from transformers import pipeline
+
+pipe = pipeline('text2text-generation', model='mohammedaly2222002/t5-small-squad-qg')
+
+context = """During the 2011–12 season, he set the La Liga and European records\
+for most goals scored in a single season, while establishing himself as Barcelona's\
+all-time top scorer. The following two seasons, Messi finished second for the Ballon\
+d'Or behind Cristiano Ronaldo (his perceived career rival), before regaining his best\
+form during the 2014–15 campaign, becoming the all-time top scorer in La Liga and \
+leading Barcelona to a historic second treble, after which he was awarded a fifth \
+Ballon d'Or in 2015. Messi assumed captaincy of Barcelona in 2018, and won a record \
+sixth Ballon d'Or in 2019. Out of contract, he signed for French club Paris Saint-Germain\
+in August 2021, spending two seasons at the club and winning Ligue 1 twice. Messi \
+joined American club Inter Miami in July 2023, winning the Leagues Cup in August of that year.
+"""
+
+answer_highlighted_context = highlight_answer(context=context, answer='Inter Miami')
+prompt = prepare_instruction(answer_highlighted_context)
+```
+
+This will be the final prompt:
+```
+Generate a question whose answer is highlighted by <h> from the context delimited by the triple backticks
+context:
+```During the 2011–12 season, he set the La Liga and European records\
+for most goals scored in a single season, while establishing himself as Barcelona's\
+all-time top scorer. The following two seasons, Messi finished second for the Ballon\
+d'Or behind Cristiano Ronaldo (his perceived career rival), before regaining his best\
+form during the 2014–15 campaign, becoming the all-time top scorer in La Liga and \
+leading Barcelona to a historic second treble, after which he was awarded a fifth \
+Ballon d'Or in 2015. Messi assumed captaincy of Barcelona in 2018, and won a record\
+ sixth Ballon d'Or in 2019. Out of contract, he signed for French club Paris Saint-Germain\
+in August 2021, spending two seasons at the club and winning Ligue 1 twice. Messi \
+joined American club  <h> Inter Miami <h> in July 2023, winning the Leagues Cup in August of that year.```
+```
+
+3. Use the loaded `pipeline` to generate questions their answer is `Inter Miami`:
+
+```Python
+outputs = pipe(prompt, num_return_sequences=3, num_beams=5, num_beam_groups=5, diversity_penalty=1.0)
+for output in outputs:
+    print(output['generated_text'])
+```
+
+Result:
+```
+1. What club did Messi join in the 2023 season?
+2. What was Messi's name of the club that won the Leagues Cup on July 20?
+3. What club did Messi join in the Leagues Cup in July 2023?
+```
 
 ## Training and evaluation data
 
@@ -70,4 +164,4 @@ The following hyperparameters were used during training:
 - Transformers 4.38.1
 - Pytorch 2.1.2
 - Datasets 2.13.1
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2