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Datasets:
hyrinmansoor
/
text2frappe-s2-flan

Tags:
erp
excat_field_prediction
changAI
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/hyrinmansoor/text2frappe-s2-flan@189ea2917779b2c48a17727343470fb921bf8dfe/train.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/hyrinmansoor/text2frappe-s2-flan@189ea2917779b2c48a17727343470fb921bf8dfe/train.json.

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text2frappe-s2-flan-field

🚀 ChangAI – Stage 2 (Field Selection with Flan-T5)

This model is part of the ChangAI pipeline for converting natural-language ERP questions into executable Frappe SQL queries.

📖 What it does

Given a Doctype and a user question, this model selects the most relevant fields from the Frappe metadata that should be used to answer the query.

It acts as the decision maker after field ranking:

  • Takes the ranked fields from SBERT
  • Filters & finalizes which fields to use
  • Ensures required defaults like name are included when needed

This helps Stage 3 (Query Generator) build valid, executable SQL.

🏗️ Model Architecture

  • Base model: google/flan-t5-base
  • Fine-tuned task: Conditional generation (seq2seq)
  • Input format: JSON-like text containing question + doctype + candidate fields
  • Output format: A list of selected fields (comma-separated)

🔧 Example

Input:

Doctype: Sales Invoice
Question: show overdue invoices with customer name
Top fields: [name, posting_date, due_date, customer_name, outstanding_amount, company, status]

Output:

name, customer_name, due_date, outstanding_amount

📂 Training Data

  • Synthetic + curated dataset based on ERPNext Doctype metadata

  • Training samples follow this structure:

    • Anchor: question + doctype
    • Positives: required fields
    • Negatives: irrelevant fields

  • Rules enforced:

    • Non-count queries always include name
    • Only valid metadata fields are used

🚀 Usage 

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "hyrinmansoor/text2frappe-s2-flan-field"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

inp = """Doctype: Sales Invoice
Question: show overdue invoices with customer name
Candidate fields: [name, posting_date, due_date, customer_name, outstanding_amount, company, status]"""

inputs = tokenizer(inp, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# -> "name, customer_name, due_date, outstanding_amount"

🔗 Related Models

📌 Notes

  • Works best with ERPNext v14+ doctypes
  • Can be extended to custom doctypes by retraining with updated metadata
  • Part of the ChangAI open-source project: plain-language ERP queries → SQL
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