akshaypardhanani/delstilbert-ner-finetuned

DistillBERT

Fine Tuning DistillBERT on the FiNER-139 dataset

Model

Checkppoints

The model Checkpoints are in the distilbert-finetuned-ner directory at the root. checkpoint-1407 is the one on which all the evaluation has been done

Saved Model

This is in the distilber-finer-tuned directory. TODO: To be published to HuggingFace

ONNX runtime

See TODOs

Directory Structure

----DISTILLBERT
  |_ distilbert-finetuned-ner
  |_ src
  |  |_ data_preparation
  |  |_ training
  |_ DatExploration.ipynb

Data Exploration

The file in which we examine the dataset and see the distribution of the tokens and of the labels is DatExploration.ipynb The 4 labels chosen to evaluate on are:

• B-ShareBasedCompensationArrangementByShareBasedPaymentAwardAwardVestingPeriod1
• I-ShareBasedCompensationArrangementByShareBasedPaymentAwardAwardVestingPeriod1
• B-DebtInstrumentMaturityDate
• I-DebtInstrumentMaturityDate

The obtained metrics on model evaluation are:

eval_loss	eval_precision	eval_recall	eval_f1	eval_accuracy
0.044684	0.770968	0.784893	0.777868	0.976305

Dependencies

All the dependencies are defined in requirements.txt They should be installed in a new venv by running python -m pip install -r requirements.txt from the repo root.

TODOs

1. Export to ONNX
2. Evaluate performance on ONNX runtime compared to the original Distil-BERT model
3. Write to Hugging Face