# Model Card for SVLM

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 Description

- **Developed by:** @binarybardakshat
- **Model type:** Seq2Seq Language Model (BART-based)
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model:** facebook/bart-base

### Model Sources

- **Repository:** [More Information Needed]

## Uses

### Direct Use

This model can be directly used to answer questions based on research data from ACL papers. It is suitable for academic and research purposes.

### Out-of-Scope Use

The model may not work well for general conversation or non-research-related queries.

## Bias, Risks, and Limitations

The model may carry biases present in the training data, which consists of ACL research papers. It might not generalize well outside this domain.

### Recommendations

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 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**

#### Software

- **TensorFlow**
- **Transformers**
- **Safetensors**