Update README.md

README.md

@@ -1,42 +1,56 @@
----
-license: openrail
-datasets:
-- Binarybardakshat/SVLM-ACL-DATASET
-language:
-- en
-library_name: transformers
----
-# SVLM: A Question-Answering Model for ACL Research Papers
-
-This model, `SVLM`, is designed to answer questions based on research papers from the ACL dataset. It leverages the BART architecture to generate precise answers from scientific abstracts.
+# SVLM (Scientific Virtual Language Model)
+
+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.
 
 ## Model Details
 
-- **Model Architecture:** BART (Bidirectional and Auto-Regressive Transformers)
-- **Framework:** TensorFlow
-- **Dataset:** [Binarybardakshat/SVLM-ACL-DATASET](https://huggingface.co/datasets/Binarybardakshat/SVLM-ACL-DATASET)
-- **Author:** @binarybardakshat (Akshat Shukla)
-- **Purpose:** The model is trained to provide answers to questions from the ACL research paper dataset.
+- **Model Architecture:** BART (Base)
+- **Libraries Used:**
+  - `transformers`
+  - `tensorflow`
+  - `datasets`
+  - `safetensors`
+  - `pandas`
 
-## Usage
+- **Training Framework:** TensorFlow
+- **Data Source:** [SVLM-ACL-DATASET](https://huggingface.co/datasets/Binarybardakshat/SVLM-ACL-DATASET)
 
-To use this model with the Hugging Face Interface API:
+## Usage
 
 ```python
-from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
+from transformers import pipeline
 
 # Load the model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("binarybardakshat/SVLM")
-model = TFAutoModelForSeq2SeqLM.from_pretrained("binarybardakshat/SVLM")
+qa_pipeline = pipeline("question-answering", model="Binarybardakshat/svlm", tokenizer="Binarybardakshat/svlm")
+
+# 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)
+
+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.
+
+## Model Details
+
+- **Model Architecture:** BART (Base)
+- **Libraries Used:**
+  - `transformers`
+  - `tensorflow`
+  - `datasets`
+  - `safetensors`
+  - `pandas`
+
+- **Training Framework:** TensorFlow
+- **Data Source:** [SVLM-ACL-DATASET](https://huggingface.co/datasets/Binarybardakshat/SVLM-ACL-DATASET)
 
-# Example input
-input_text = "What is the main contribution of the paper titled 'Your Paper Title'?"
+## Usage
 
-# Tokenize input
-inputs = tokenizer(input_text, return_tensors="tf", padding=True, truncation=True)
+```python
+from transformers import pipeline
 
-# Generate answer
-outputs = model.generate(inputs.input_ids, max_length=50, num_beams=5, early_stopping=True)
-answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Load the model and tokenizer
+qa_pipeline = pipeline("question-answering", model="Binarybardakshat/svlm", tokenizer="Binarybardakshat/svlm")
 
-print("Answer:", answer)
+# Ask a question
+result = qa_pipeline(question="What is the purpose of this research?", context="Context of the research paper...")
+print(result)