Update README.md

README.md

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 ---
-license: mit
+license: apache-2.0
+datasets:
+- PipableAI/pip-txt-to-sql-spider-bird-dataset
+language:
+- en
+metrics:
+- accuracy
+tags:
+- document
+- code
+- text2sql
+- instruction_tuned
+- basemodel
+- jax
+- pytorch
+- tensorflow
+- text-generation-inference
+library_name: transformers
+pipeline_tag: text-generation
+widget:
+- text: "<schema>CREATE TABLE system(JobID: String,GID: String, UID: String, Start:Time(yyyy/mm/dd), End: Time,ElapsedRaw: Time, CPUTimeRAW: Time,NCPUS: Number,NNodes: Number, NodeList: List,  State:String, Timelimit: Time);</schema><question>Get UID and job id for Jobs that started on Jan 20 , 2023 ended on feb 14 2023 and has job id 20</question><sql>"
+  example_title: "example"
+
 ---
+# pip-parse
+
+[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
+
+[colab_notebook]()
+
+## What have we built?
+A 1.3 bn code documentation model that outperforms most models on documenting codes and making your in-house libs ready for LLM and RAG pipelines.
+We have also open sourced a parsing lib for the same , together the lib and model can turn your codebase to functional parse tree ready to be consumed by LLMs to execute complex tasks.
+This is a further trained version of pip-sql-1.3b.
+
+
+
+## How we built it?
+
+We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
+Loss behaviour in the set up mentioned above - 
+
+
+## License
+The model is open source under apache 2.0. License
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers
+```
+
+### Prompt
+```python
+prompt = f"""<code>{schema}</code>
+<question>Document the code above</question>
+<doc>"""
+```
+
+### PyTorch
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-parser")
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-parser")
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=300)
+tokenizer.decode(outputs[0], skip_special_tokens=True).split('<doc>')[-1].split('</doc>')[0]
+```
+
+
+
+## Examples
+
+### Code
+```python
+<code>
+###########################
+# Generate Analytical Model
+###########################
+##################################################
+# func: get_np_array_transition_probability_matrix
+##################################################
+def get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix):
+    print('np_array_A_matrix:')
+    print(np_array_A_matrix)
+    #####################################################
+    # Perturb the adjacency matrix to avoid singularities
+    #####################################################
+    np_array_A_matrix += (np.full((int_num_states, int_num_states), float_eps) - (np.identity(int_num_states) * float_eps))
+    print('np_array_A_matrix:')
+    print(np_array_A_matrix)
+    print('np_array_D_matrix:')
+    np_array_D_matrix = np.diag(np.sum(np_array_A_matrix, axis=1))
+    print(np_array_D_matrix)
+    print('np_array_D_matrix_inv:')
+    np_array_D_matrix_inv = np.linalg.inv(np_array_D_matrix)
+    print(np_array_D_matrix_inv)
+    print('\n\n')
+    print('np_array_P_matrix:')
+    np_array_P_matrix = np.dot(np_array_D_matrix_inv, np_array_A_matrix)
+    print(np_array_P_matrix)
+    print('np.sum(np_array_P_matrix, axis=1):')
+    print(np.sum(np_array_P_matrix, axis=1))
+    print('\n\n')
+    return np_array_P_matrix
+##################################################
+# func: get_np_array_perron_frobenius_eigen_vector
+##################################################
+def get_np_array_perron_frobenius_matrix(int_num_states, np_array_P_matrix):
+    np_array_perron_frobenius_matrix = np.linalg.matrix_power(np_array_P_matrix,1000)
+    np_array_perron_frobenius_vector = np_array_perron_frobenius_matrix[0,:]
+    print('np_array_perron_frobenius_matrix:')
+    print(np_array_perron_frobenius_matrix)
+    print('np.sum(np_array_perron_frobenius_matrix, axis=1):')
+    print(np.sum(np_array_perron_frobenius_matrix, axis=1))
+    print('np.sum(np_array_perron_frobenius_matrix, axis=0):')
+    print(np.sum(np_array_perron_frobenius_matrix, axis=0))
+    print('np.sum(np_array_perron_frobenius_matrix, axis=0)/int_num_states:')
+    print(np.sum(np_array_perron_frobenius_matrix, axis=0)/int_num_states)
+    print('np.dot(np_array_perron_frobenius_vector, np_array_P_matrix):')
+    print(np.dot(np_array_perron_frobenius_vector, np_array_P_matrix))
+    print('np_array_perron_frobenius_vector:')
+    print(np_array_perron_frobenius_vector)
+    print('\n\n')
+    return np_array_perron_frobenius_vector, np_array_perron_frobenius_matrix
+#############################
+# func: get_np_array_Z_matrix
+#############################
+def get_np_array_Z_matrix(int_num_states, np_array_P_matrix, np_array_perron_frobenius_matrix):
+    np_array_Z_matrix = np.linalg.inv(np.identity(int_num_states) - np_array_P_matrix + np_array_perron_frobenius_matrix)
+    print('np_array_Z_matrix:')
+    print(np_array_Z_matrix)
+    print('\n\n')
+    return(np_array_Z_matrix)
+#############################
+# func: get_np_array_H_matrix
+#############################
+def get_np_array_H_matrix(int_num_states, np_array_Z_matrix, np_array_perron_frobenius_vector):
+    np_array_H_matrix = np.zeros([int_num_states, int_num_states])
+    for i in range(int_num_states):
+        for j in range(int_num_states):
+            np_array_H_matrix[i][j] = (np_array_Z_matrix[j][j] - np_array_Z_matrix[i][j])/np_array_perron_frobenius_vector[j]
+    print('np_array_H_matrix:')
+    print(np_array_H_matrix)
+    print('\n\n')
+    return np_array_H_matrix
+###########
+# func: run
+###########
+def run(np_array_A_matrix):
+    int_num_states = len(np_array_A_matrix)
+    np_array_P_matrix = get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix)
+    np_array_perron_frobenius_vector, np_array_perron_frobenius_matrix = get_np_array_perron_frobenius_matrix(int_num_states, np_array_P_matrix)
+    np_array_Z_matrix = get_np_array_Z_matrix(int_num_states, np_array_P_matrix, np_array_perron_frobenius_matrix)
+    np_array_H_matrix = get_np_array_H_matrix(int_num_states, np_array_Z_matrix, np_array_perron_frobenius_vector)
+    return(np_array_H_matrix)
+    <question>Document the python code above.
+    </question><doc>
+```
+
+### Response
+What are the email address, town and county of the customers who are of the least common gender?
+```python
+The Python code provided is used to generate an analytical model for a Markov chain with a given adjacency matrix.
+The model is then used to compute the Perron-Frobenius eigenvector and the corresponding matrix. The resulting matrices are then used to compute the Z-matrix and
+the H-matrix. The H-matrix is then returned as the output of the function. The code is designed to handle large matrices and perform computations efficiently.
+The matrices are manipulated using numpy's powerful and efficient numerical computation library.
+The code also includes comments to explain the functionality of each part of the code. 
+```
+
+### Team
+Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya