Update README.md

README.md

@@ -1,56 +1,99 @@
-# SVLM (Scientific Virtual Language Model)
+# Model Card for SVLM
 
-This model is designed to answer questions based on research papers from the ACL Anthology. The model uses a Seq2Seq approach and was fine-tuned on the `SVLM-ACL-DATASET` dataset, focusing on generating high-quality responses related to research topics.
+This model is a Seq2Seq Language Model (SVLM) fine-tuned to answer questions from the ACL research paper dataset. It generates responses related to academic research questions, making it useful for research and academic inquiry.
 
 ## Model Details
 
-- **Model Architecture:** BART (Base)
-- **Libraries Used:**
- - `transformers`
- - `tensorflow`
- - `datasets`
- - `safetensors`
- - `pandas`
+### Model Description
 
-- **Training Framework:** TensorFlow
-- **Data Source:** [SVLM-ACL-DATASET](https://huggingface.co/datasets/Binarybardakshat/SVLM-ACL-DATASET)
+- **Developed by:** @binarybardakshat
+- **Model type:** Seq2Seq Language Model (BART-based)
+- **Language(s) (NLP):** English
+- **License:** [More Information Needed]
+- **Finetuned from model:** facebook/bart-base
 
-## Usage
+### Model Sources
 
-```python
-from transformers import pipeline
+- **Repository:** [More Information Needed]
 
-# Load the model and tokenizer
-qa_pipeline = pipeline("question-answering", model="Binarybardakshat/svlm", tokenizer="Binarybardakshat/svlm")
+## Uses
 
-# Ask a question
-result = qa_pipeline(question="What is the purpose of this research?", context="Context of the research paper...")
-print(result)
-# SVLM (Scientific Virtual Language Model)
+### Direct Use
 
-This model is designed to answer questions based on research papers from the ACL Anthology. The model uses a Seq2Seq approach and was fine-tuned on the `SVLM-ACL-DATASET` dataset, focusing on generating high-quality responses related to research topics.
+This model can be directly used to answer questions based on research data from ACL papers. It is suitable for academic and research purposes.
 
-## Model Details
+### Out-of-Scope Use
+
+The model may not work well for general conversation or non-research-related queries.
+
+## Bias, Risks, and Limitations
 
-- **Model Architecture:** BART (Base)
-- **Libraries Used:**
- - `transformers`
- - `tensorflow`
- - `datasets`
- - `safetensors`
- - `pandas`
+The model may carry biases present in the training data, which consists of ACL research papers. It might not generalize well outside this domain.
 
-- **Training Framework:** TensorFlow
-- **Data Source:** [SVLM-ACL-DATASET](https://huggingface.co/datasets/Binarybardakshat/SVLM-ACL-DATASET)
+### Recommendations
 
-## Usage
+Users should be cautious of biases and ensure that outputs align with their academic requirements.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model:
 
 ```python
-from transformers import pipeline
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("path_to_your_tokenizer")
+model = AutoModelForSeq2SeqLM.from_pretrained("path_to_your_model")
+## Training Details
+
+### Training Data
+
+The model was trained using the ACL dataset, which consists of research papers focused on computational linguistics.
+
+### Training Procedure
+
+#### Training Hyperparameters
+
+- **Training regime:** fp32
+- **Learning rate:** 2e-5
+- **Epochs:** 3
+- **Batch size:** 8
+
+## Evaluation
+
+### Testing Data
+
+The model was evaluated on a subset of the ACL dataset, focusing on research-related questions.
+
+### Metrics
+
+- **Accuracy**
+- **Loss**
+
+### Results
+
+The model performs best in research-related question-answering tasks. Further evaluation metrics will be added as the model is used more widely.
+
+## Environmental Impact
+
+- **Hardware Type:** GPU (NVIDIA V100)
+- **Hours used:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model is based on BART architecture, designed to perform sequence-to-sequence tasks like text summarization and translation.
+
+### Compute Infrastructure
+
+#### Hardware
+
+- **NVIDIA V100 GPU**
 
-# Load the model and tokenizer
-qa_pipeline = pipeline("question-answering", model="Binarybardakshat/svlm", tokenizer="Binarybardakshat/svlm")
+#### Software
 
-# Ask a question
-result = qa_pipeline(question="What is the purpose of this research?", context="Context of the research paper...")
-print(result)
+- **TensorFlow**
+- **Transformers**
+- **Safetensors**