distilbert-base-cased-pii-en / README.md

Update README.md

f804b23 verified 5 months ago

11.4 kB

	---
	license: apache-2.0
	base_model: distilbert-base-cased
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: distilbert-base-cased-pii-en
	results: []
	datasets:
	- ai4privacy/pii-masking-300k
	language:
	- en
	pipeline_tag: token-classification

	widget:
	- text: "My name is Yoni Go and I live in Israel. My phone number is 054-1234567"

	inference:
	parameters:
	aggregation_strategy: "first"
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# distilbert-base-cased-pii-en

	This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on English samples from [ai4privacy/pii-masking-300k](https://huggingface.co/datasets/ai4privacy/pii-masking-300k).

	Usage:
	```python
	from transformers import pipeline

	pipe = pipeline("token-classification", model="yonigo/distilbert-base-cased-pii-en", aggregation_strategy="first")
	pipe("My name is Yoni Go and I live in Israel. My phone number is 054-1234567")
	```

	training code [git](https://github.com/yonigottesman/pii-model)




	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bod F1 \| Building F1 \| Cardissuer F1 \| City F1 \| Country F1 \| Date F1 \| Driverlicense F1 \| Email F1 \| Geocoord F1 \| Givenname1 F1 \| Givenname2 F1 \| Idcard F1 \| Ip F1 \| Lastname1 F1 \| Lastname2 F1 \| Lastname3 F1 \| Pass F1 \| Passport F1 \| Postcode F1 \| Secaddress F1 \| Sex F1 \| Socialnumber F1 \| State F1 \| Street F1 \| Tel F1 \| Time F1 \| Title F1 \| Username F1 \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:-----------:\|:-------------:\|:-------:\|:----------:\|:-------:\|:----------------:\|:--------:\|:-----------:\|:-------------:\|:-------------:\|:---------:\|:------:\|:------------:\|:------------:\|:------------:\|:-------:\|:-----------:\|:-----------:\|:-------------:\|:------:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------:\|:--------:\|:-----------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.23 \| 2.1368 \| 1000 \| 0.1111 \| 0.8804 \| 0.9218 \| 0.0 \| 0.5854 \| 0.8890 \| 0.8009 \| 0.5896 \| 0.9469 \| 0.7002 \| 0.4149 \| 0.0 \| 0.5740 \| 0.9056 \| 0.5230 \| 0.0 \| 0.0 \| 0.6551 \| 0.6301 \| 0.7819 \| 0.5871 \| 0.8396 \| 0.7137 \| 0.8771 \| 0.6466 \| 0.8567 \| 0.9185 \| 0.7411 \| 0.8202 \| 0.7127 \| 0.7640 \| 0.7374 \| 0.9723 \|
	\| 0.0687 \| 4.2735 \| 2000 \| 0.0541 \| 0.9365 \| 0.9688 \| 0.0 \| 0.9115 \| 0.9434 \| 0.8841 \| 0.8530 \| 0.9768 \| 0.9698 \| 0.7276 \| 0.3073 \| 0.8720 \| 0.9701 \| 0.6430 \| 0.1854 \| 0.0 \| 0.8169 \| 0.8857 \| 0.9638 \| 0.9617 \| 0.9312 \| 0.8927 \| 0.9418 \| 0.9231 \| 0.9438 \| 0.9461 \| 0.9232 \| 0.9138 \| 0.8698 \| 0.9020 \| 0.8856 \| 0.9862 \|
	\| 0.0452 \| 6.4103 \| 3000 \| 0.0469 \| 0.9550 \| 0.9753 \| 0.0 \| 0.9404 \| 0.9620 \| 0.9014 \| 0.9004 \| 0.9831 \| 0.9721 \| 0.7664 \| 0.4606 \| 0.9099 \| 0.97 \| 0.6838 \| 0.3818 \| 0.0 \| 0.8611 \| 0.9079 \| 0.9757 \| 0.9715 \| 0.9661 \| 0.9197 \| 0.9674 \| 0.9435 \| 0.9448 \| 0.96 \| 0.9354 \| 0.9357 \| 0.8975 \| 0.9212 \| 0.9092 \| 0.9883 \|
	\| 0.0311 \| 8.5470 \| 4000 \| 0.0434 \| 0.9581 \| 0.9753 \| 0.0 \| 0.9481 \| 0.9641 \| 0.8973 \| 0.9217 \| 0.9833 \| 0.9745 \| 0.8085 \| 0.6085 \| 0.9232 \| 0.9781 \| 0.7519 \| 0.4891 \| 0.0849 \| 0.8685 \| 0.9280 \| 0.9771 \| 0.9704 \| 0.9659 \| 0.9278 \| 0.9729 \| 0.9549 \| 0.9563 \| 0.9594 \| 0.9564 \| 0.9407 \| 0.9117 \| 0.9344 \| 0.9229 \| 0.9897 \|
	\| 0.023 \| 10.6838 \| 5000 \| 0.0416 \| 0.9578 \| 0.9778 \| 0.0 \| 0.9503 \| 0.9639 \| 0.9043 \| 0.9245 \| 0.9804 \| 0.9767 \| 0.8277 \| 0.6607 \| 0.9031 \| 0.9836 \| 0.7790 \| 0.5779 \| 0.25 \| 0.8791 \| 0.9276 \| 0.9767 \| 0.9660 \| 0.9708 \| 0.9353 \| 0.9749 \| 0.9610 \| 0.9681 \| 0.9611 \| 0.9545 \| 0.9382 \| 0.9185 \| 0.9363 \| 0.9273 \| 0.9903 \|
	\| 0.016 \| 12.8205 \| 6000 \| 0.0436 \| 0.9607 \| 0.9778 \| 0.0 \| 0.9516 \| 0.9698 \| 0.9027 \| 0.9274 \| 0.9781 \| 0.9677 \| 0.8472 \| 0.6913 \| 0.9259 \| 0.9877 \| 0.7857 \| 0.5925 \| 0.3819 \| 0.8853 \| 0.9377 \| 0.9731 \| 0.9688 \| 0.9752 \| 0.9461 \| 0.9756 \| 0.9594 \| 0.9613 \| 0.9568 \| 0.9545 \| 0.9447 \| 0.9247 \| 0.9396 \| 0.9321 \| 0.9907 \|
	\| 0.012 \| 14.9573 \| 7000 \| 0.0447 \| 0.9598 \| 0.9808 \| 0.0 \| 0.9558 \| 0.9737 \| 0.9070 \| 0.9339 \| 0.9719 \| 0.9654 \| 0.8482 \| 0.7009 \| 0.9280 \| 0.9859 \| 0.7879 \| 0.6027 \| 0.4729 \| 0.8909 \| 0.9413 \| 0.9781 \| 0.9750 \| 0.9740 \| 0.9431 \| 0.9778 \| 0.9624 \| 0.9623 \| 0.9665 \| 0.9555 \| 0.9403 \| 0.9275 \| 0.9413 \| 0.9344 \| 0.9911 \|
	\| 0.0086 \| 17.0940 \| 8000 \| 0.0487 \| 0.9584 \| 0.9814 \| 0.0 \| 0.9529 \| 0.9733 \| 0.9114 \| 0.9350 \| 0.9857 \| 0.9767 \| 0.8493 \| 0.7059 \| 0.9306 \| 0.9829 \| 0.7918 \| 0.6216 \| 0.5292 \| 0.8934 \| 0.9330 \| 0.9790 \| 0.9766 \| 0.9750 \| 0.9395 \| 0.9747 \| 0.9624 \| 0.9605 \| 0.9631 \| 0.9580 \| 0.9399 \| 0.9282 \| 0.9423 \| 0.9352 \| 0.9908 \|
	\| 0.0062 \| 19.2308 \| 9000 \| 0.0509 \| 0.9596 \| 0.9795 \| 0.0 \| 0.9594 \| 0.9708 \| 0.9128 \| 0.9299 \| 0.9872 \| 0.9837 \| 0.8491 \| 0.7188 \| 0.9270 \| 0.9859 \| 0.7923 \| 0.6468 \| 0.5371 \| 0.8919 \| 0.9369 \| 0.9783 \| 0.9756 \| 0.9749 \| 0.9382 \| 0.9778 \| 0.9650 \| 0.9647 \| 0.9653 \| 0.9559 \| 0.9461 \| 0.9337 \| 0.9394 \| 0.9365 \| 0.9911 \|
	\| 0.0045 \| 21.3675 \| 10000 \| 0.0548 \| 0.9559 \| 0.9774 \| 0.0 \| 0.9524 \| 0.9720 \| 0.9080 \| 0.9348 \| 0.9827 \| 0.9814 \| 0.8446 \| 0.7117 \| 0.9271 \| 0.9800 \| 0.7977 \| 0.6428 \| 0.5266 \| 0.8964 \| 0.9351 \| 0.9754 \| 0.9716 \| 0.9728 \| 0.9478 \| 0.9757 \| 0.9584 \| 0.9698 \| 0.9587 \| 0.9548 \| 0.9423 \| 0.9242 \| 0.9457 \| 0.9348 \| 0.9907 \|
	\| 0.0036 \| 23.5043 \| 11000 \| 0.0560 \| 0.9594 \| 0.9781 \| 0.0 \| 0.9575 \| 0.9720 \| 0.9121 \| 0.9367 \| 0.9814 \| 0.9814 \| 0.8504 \| 0.7209 \| 0.9317 \| 0.9807 \| 0.7922 \| 0.6507 \| 0.5918 \| 0.8864 \| 0.9380 \| 0.9769 \| 0.9722 \| 0.9745 \| 0.9399 \| 0.9771 \| 0.9628 \| 0.9675 \| 0.9618 \| 0.9581 \| 0.9446 \| 0.9288 \| 0.9434 \| 0.9361 \| 0.9910 \|
	\| 0.0026 \| 25.6410 \| 12000 \| 0.0576 \| 0.9596 \| 0.9798 \| 0.0 \| 0.9575 \| 0.9732 \| 0.9130 \| 0.9308 \| 0.9831 \| 0.9791 \| 0.8471 \| 0.7104 \| 0.9268 \| 0.9836 \| 0.7967 \| 0.6563 \| 0.6222 \| 0.8982 \| 0.9345 \| 0.9771 \| 0.9739 \| 0.9733 \| 0.9402 \| 0.9771 \| 0.9673 \| 0.9656 \| 0.9642 \| 0.9576 \| 0.9480 \| 0.9288 \| 0.9446 \| 0.9366 \| 0.9910 \|
	\| 0.002 \| 27.7778 \| 13000 \| 0.0608 \| 0.9555 \| 0.9796 \| 0.0 \| 0.9561 \| 0.9717 \| 0.9045 \| 0.9319 \| 0.9848 \| 0.9791 \| 0.8488 \| 0.7157 \| 0.9268 \| 0.9852 \| 0.7909 \| 0.6580 \| 0.6039 \| 0.8900 \| 0.9360 \| 0.9802 \| 0.9717 \| 0.9750 \| 0.9361 \| 0.9778 \| 0.9646 \| 0.9683 \| 0.9615 \| 0.9565 \| 0.9465 \| 0.9279 \| 0.9433 \| 0.9355 \| 0.9909 \|
	\| 0.0016 \| 29.9145 \| 14000 \| 0.0601 \| 0.9573 \| 0.9801 \| 0.0 \| 0.9589 \| 0.9722 \| 0.9135 \| 0.9353 \| 0.9848 \| 0.9837 \| 0.8499 \| 0.7202 \| 0.9316 \| 0.9871 \| 0.7942 \| 0.6677 \| 0.6432 \| 0.9017 \| 0.9402 \| 0.9799 \| 0.9744 \| 0.9740 \| 0.9455 \| 0.9783 \| 0.9631 \| 0.9719 \| 0.9645 \| 0.9600 \| 0.9482 \| 0.9331 \| 0.9443 \| 0.9387 \| 0.9913 \|
	\| 0.0013 \| 32.0513 \| 15000 \| 0.0613 \| 0.9606 \| 0.9798 \| 0.0 \| 0.9571 \| 0.9739 \| 0.9155 \| 0.9365 \| 0.9840 \| 0.9791 \| 0.8466 \| 0.7221 \| 0.9321 \| 0.9861 \| 0.7948 \| 0.6622 \| 0.6281 \| 0.9017 \| 0.9407 \| 0.9787 \| 0.9739 \| 0.9740 \| 0.9475 \| 0.9774 \| 0.9632 \| 0.9693 \| 0.9644 \| 0.9594 \| 0.9469 \| 0.9313 \| 0.9457 \| 0.9384 \| 0.9912 \|
	\| 0.001 \| 34.1880 \| 16000 \| 0.0639 \| 0.9611 \| 0.9808 \| 0.0 \| 0.9601 \| 0.9729 \| 0.9149 \| 0.9337 \| 0.9867 \| 0.9814 \| 0.8483 \| 0.7269 \| 0.9307 \| 0.9838 \| 0.7956 \| 0.6627 \| 0.6154 \| 0.9025 \| 0.9397 \| 0.9797 \| 0.9733 \| 0.9738 \| 0.9383 \| 0.9776 \| 0.9636 \| 0.9674 \| 0.9637 \| 0.9576 \| 0.9480 \| 0.9304 \| 0.9457 \| 0.9380 \| 0.9911 \|
	\| 0.0009 \| 36.3248 \| 17000 \| 0.0621 \| 0.9622 \| 0.9811 \| 0.0 \| 0.9604 \| 0.9741 \| 0.9156 \| 0.9359 \| 0.9855 \| 0.9814 \| 0.8510 \| 0.7273 \| 0.9319 \| 0.9859 \| 0.7991 \| 0.6646 \| 0.6413 \| 0.8999 \| 0.9393 \| 0.9789 \| 0.9739 \| 0.9740 \| 0.9427 \| 0.9789 \| 0.9653 \| 0.9687 \| 0.9637 \| 0.9597 \| 0.9460 \| 0.9324 \| 0.9456 \| 0.9390 \| 0.9913 \|
	\| 0.0008 \| 38.4615 \| 18000 \| 0.0631 \| 0.9620 \| 0.9801 \| 0.0 \| 0.9582 \| 0.9744 \| 0.9190 \| 0.9350 \| 0.9853 \| 0.9814 \| 0.8514 \| 0.7253 \| 0.9298 \| 0.9848 \| 0.7992 \| 0.6677 \| 0.6434 \| 0.8992 \| 0.9401 \| 0.9797 \| 0.9739 \| 0.9731 \| 0.9421 \| 0.9789 \| 0.9653 \| 0.9681 \| 0.9643 \| 0.9592 \| 0.9462 \| 0.9319 \| 0.9457 \| 0.9388 \| 0.9913 \|
	\| 0.0007 \| 40.5983 \| 19000 \| 0.0633 \| 0.9615 \| 0.9806 \| 0.0 \| 0.9589 \| 0.9741 \| 0.9170 \| 0.9358 \| 0.9861 \| 0.9814 \| 0.8501 \| 0.7268 \| 0.9316 \| 0.9857 \| 0.7973 \| 0.6662 \| 0.6393 \| 0.9002 \| 0.9405 \| 0.9797 \| 0.9745 \| 0.9738 \| 0.9442 \| 0.9796 \| 0.9658 \| 0.9679 \| 0.9644 \| 0.9586 \| 0.9469 \| 0.9323 \| 0.9459 \| 0.9391 \| 0.9913 \|
	\| 0.0008 \| 42.7350 \| 20000 \| 0.0635 \| 0.9622 \| 0.9808 \| 0.0 \| 0.9589 \| 0.9741 \| 0.9176 \| 0.9355 \| 0.9855 \| 0.9814 \| 0.8496 \| 0.7261 \| 0.9306 \| 0.9850 \| 0.7974 \| 0.6672 \| 0.6393 \| 0.9005 \| 0.9405 \| 0.9794 \| 0.9745 \| 0.9738 \| 0.9437 \| 0.9794 \| 0.9651 \| 0.9674 \| 0.9638 \| 0.9587 \| 0.9469 \| 0.9318 \| 0.9460 \| 0.9388 \| 0.9913 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.3.1+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1