Update README.md

README.md

@@ -10,198 +10,142 @@ datasets:
 - flytech/python-codes-25k
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# BART-LARGE-CNN fine-tuned on SYNTHETIC_TEXT_TO_SQL
 
+Generate SQL query from Natural Language question with a SQL context.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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. -->
+BART from facebook/bart-large-cnn is fintuned on gretelai/synthetic_text_to_sql dataset to generate SQL from NL and SQL context
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
-## Uses
+- **Model type:** BART
+- **Language(s) (NLP):** English
+- **License:** openrail
+- **Finetuned from model [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct.)**
+- **Dataset:** [gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Intended uses & limitations
 
-### Direct Use
+Addressing the power of LLM in fintuned downstream task. Implemented as a personal Project.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### How to use
 
-[More Information Needed]
+```python
+query_question_with_context = """sql_prompt: Which economic diversification efforts in
+the 'diversification' table have a higher budget than the average budget for all economic diversification efforts in the 'budget' table?
+sql_context: CREATE TABLE diversification (id INT, effort VARCHAR(50), budget FLOAT); CREATE TABLE
+budget (diversification_id INT, diversification_effort VARCHAR(50), amount FLOAT);"""
+```
 
-### Downstream Use [optional]
+# Use a pipeline as a high-level helper
+```python
+from transformers import pipeline
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+sql_generator = pipeline("text2text-generation", model="SwastikM/bart-large-nl2sql")
 
-[More Information Needed]
+sql = sql_generator(query_question_with_context)[0]['generated_text']
 
-### Out-of-Scope Use
+print(sql)
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+# Load model directly
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-## Bias, Risks, and Limitations
+tokenizer = AutoTokenizer.from_pretrained("SwastikM/bart-large-nl2sql")
+model = AutoModelForSeq2SeqLM.from_pretrained("SwastikM/bart-large-nl2sql")
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+inputs = tokenizer(query_question_with_context, return_tensors="pt").input_ids
+outputs = model.generate(inputs, max_new_tokens=100, do_sample=False)
 
-[More Information Needed]
+sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(sql)
+```
 
-### 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]
+[gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+HuggingFace Accelerate with Training Loop.
 
-#### Preprocessing [optional]
+#### Preprocessing 
 
-[More Information Needed]
+- ***Encoder Input:*** "sql_prompt: " + data['sql_prompt']+" sql_context: "+data['sql_context']
+- ***Decoder Input:*** data['sql']
 
 
 #### 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]
+- **Optimizer:** AdamW
+- **lr:** 2e-5
+- **decay:** linear
+- **num_warmup_steps:** 0
+- **batch_size:** 8
+- **num_training_steps:** 12500
 
-#### 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:**
+- **GPU:** P100
 
-[More Information Needed]
 
-**APA:**
+### Citing Dataset and BaseModel
 
-[More Information Needed]
+```
+  @software{gretel-synthetic-text-to-sql-2024,
+  author = {Meyer, Yev and Emadi, Marjan and Nathawani, Dhruv and Ramaswamy, Lipika and Boyd, Kendrick and Van Segbroeck, Maarten and Grossman, Matthew and Mlocek, Piotr and Newberry, Drew},
+  title = {{Synthetic-Text-To-SQL}: A synthetic dataset for training language models to generate SQL queries from natural language prompts},
+  month = {April},
+  year = {2024},
+  url = {https://huggingface.co/datasets/gretelai/synthetic-text-to-sql}
+}
+```
 
-## Glossary [optional]
+```
+@article{DBLP:journals/corr/abs-1910-13461,
+  author    = {Mike Lewis and
+               Yinhan Liu and
+               Naman Goyal and
+               Marjan Ghazvininejad and
+               Abdelrahman Mohamed and
+               Omer Levy and
+               Veselin Stoyanov and
+               Luke Zettlemoyer},
+  title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
+               Generation, Translation, and Comprehension},
+  journal   = {CoRR},
+  volume    = {abs/1910.13461},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1910.13461},
+  eprinttype = {arXiv},
+  eprint    = {1910.13461},
+  timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+```
 
-[More Information Needed]
+## Additional Information
 
-## More Information [optional]
+- ***Github:*** [Repository](https://github.com/swastikmaiti/SwastikM-bart-large-nl2sql.git)
 
-[More Information Needed]
+## Acknowledgment
 
-## Model Card Authors [optional]
+Thanks to [@AI at Meta](https://huggingface.co/facebook) for adding the Pre Trained Model.
+Thanks to [@Gretel.ai](https://huggingface.co/gretelai) for adding the datset.
 
-[More Information Needed]
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
-### Framework versions
+Swastik Maiti