Update README.md

README.md

@@ -1,201 +1,105 @@
 ---
+license: apache-2.0
+datasets:
+- squad_v2
+- drop
+language:
+- en
 library_name: transformers
-tags: []
+tags:
+- General purpose
+- Text2text Generation
+metrics:
+- bertscore
+- accuracy
+- rouge
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-
-
+# Model Card 
+
+  
+  Base Model: facebook/bart-base
+  
+  Fine-tuned : using PEFT-LoRa 
+  
+  Datasets : squad_v2, drop
+  
+  Task: Generating questions from context and answers 
+  
+  Language: English 
+
+
+# Loading the model 
+
+
+  ```python
+    from peft import PeftModel, PeftConfig
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+    HUGGING_FACE_USER_NAME = "mou3az"
+    model_name = "Question-Generation"
+    peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
+    config = PeftConfig.from_pretrained(peft_model_id)
+    model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
+    QG_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+    QG_model = PeftModel.from_pretrained(model, peft_model_id)
+  ```
+
+
+# At inference time
+
+
+  ```python
+    def get_question(context, answer):
+        device = next(QG_model.parameters()).device
+        input_text = f"Given the context '{context}' and the answer '{answer}', what question can be asked?"
+        encoding = QG_tokenizer.encode_plus(input_text, padding=True, return_tensors="pt").to(device)
+    
+        output_tokens = QG_model.generate(**encoding, early_stopping=True, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=2, max_length=100)
+        out = QG_tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace("question:", "").strip()
+    
+        return out
+  ```
+
+
+# Training parameters and hyperparameters
+
+
+  The following were used during training:
+
+  For Lora:
+
+  r=18
+
+  alpha=8
+
+
+  For training arguments:
+
+  gradient_accumulation_steps=16
+  
+  per_device_train_batch_size=8
+  
+  per_device_eval_batch_size=8
+  
+  max_steps=3000
+  
+  warmup_steps=75
+  
+  weight_decay=0.05
+  
+  learning_rate=1e-3
+  
+  lr_scheduler_type="linear"
+
+# Performance Metrics on Evaluation Set:
+
+    for 3000 optimization steps:
+    
+    Training Loss: 292400
+    
+    Evaluation Loss: 1.244928  
+  
+    Bertscore: 0.8123
+  
+    Rouge: 0.532144
+  
+    Fuzzywizzy similarity: 0.74209