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	---
	base_model: vinai/phobert-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: disfluency-large-3
	results: []
	---

	<!-- 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. -->

	# disfluency-large-3

	This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0403
	- Precision: 0.9904
	- Recall: 0.9880
	- F1: 0.9892
	- Accuracy: 0.9962

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.0 \| 280 \| 0.0331 \| 0.9719 \| 0.9754 \| 0.9736 \| 0.9926 \|
	\| 0.0853 \| 2.0 \| 560 \| 0.0354 \| 0.9771 \| 0.9736 \| 0.9753 \| 0.9923 \|
	\| 0.0853 \| 3.0 \| 840 \| 0.0360 \| 0.9759 \| 0.9754 \| 0.9757 \| 0.9928 \|
	\| 0.0119 \| 4.0 \| 1120 \| 0.0255 \| 0.9850 \| 0.9838 \| 0.9844 \| 0.9948 \|
	\| 0.0119 \| 5.0 \| 1400 \| 0.0300 \| 0.9873 \| 0.9850 \| 0.9862 \| 0.9952 \|
	\| 0.0063 \| 6.0 \| 1680 \| 0.0412 \| 0.9848 \| 0.9742 \| 0.9795 \| 0.9927 \|
	\| 0.0063 \| 7.0 \| 1960 \| 0.0304 \| 0.9844 \| 0.9838 \| 0.9841 \| 0.9952 \|
	\| 0.0039 \| 8.0 \| 2240 \| 0.0344 \| 0.9855 \| 0.9820 \| 0.9837 \| 0.9939 \|
	\| 0.004 \| 9.0 \| 2520 \| 0.0522 \| 0.9740 \| 0.9681 \| 0.9711 \| 0.9911 \|
	\| 0.004 \| 10.0 \| 2800 \| 0.0305 \| 0.9790 \| 0.9790 \| 0.9790 \| 0.9943 \|
	\| 0.0022 \| 11.0 \| 3080 \| 0.0355 \| 0.9837 \| 0.9820 \| 0.9829 \| 0.9945 \|
	\| 0.0022 \| 12.0 \| 3360 \| 0.0400 \| 0.9795 \| 0.9772 \| 0.9783 \| 0.9935 \|
	\| 0.002 \| 13.0 \| 3640 \| 0.0394 \| 0.9826 \| 0.9814 \| 0.9820 \| 0.9943 \|
	\| 0.002 \| 14.0 \| 3920 \| 0.0452 \| 0.9795 \| 0.9772 \| 0.9783 \| 0.9930 \|
	\| 0.0015 \| 15.0 \| 4200 \| 0.0405 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9935 \|
	\| 0.0015 \| 16.0 \| 4480 \| 0.0373 \| 0.9832 \| 0.9826 \| 0.9829 \| 0.9941 \|
	\| 0.0013 \| 17.0 \| 4760 \| 0.0361 \| 0.9832 \| 0.9850 \| 0.9841 \| 0.9946 \|
	\| 0.0013 \| 18.0 \| 5040 \| 0.0447 \| 0.9807 \| 0.9790 \| 0.9798 \| 0.9937 \|
	\| 0.0013 \| 19.0 \| 5320 \| 0.0340 \| 0.9874 \| 0.9856 \| 0.9865 \| 0.9955 \|
	\| 0.0009 \| 20.0 \| 5600 \| 0.0374 \| 0.9873 \| 0.9826 \| 0.9849 \| 0.9948 \|
	\| 0.0009 \| 21.0 \| 5880 \| 0.0410 \| 0.9843 \| 0.9784 \| 0.9813 \| 0.9943 \|
	\| 0.0007 \| 22.0 \| 6160 \| 0.0275 \| 0.9892 \| 0.9862 \| 0.9877 \| 0.9961 \|
	\| 0.0007 \| 23.0 \| 6440 \| 0.0360 \| 0.9891 \| 0.9850 \| 0.9871 \| 0.9960 \|
	\| 0.0011 \| 24.0 \| 6720 \| 0.0323 \| 0.9868 \| 0.9850 \| 0.9859 \| 0.9954 \|
	\| 0.0006 \| 25.0 \| 7000 \| 0.0386 \| 0.9867 \| 0.9820 \| 0.9843 \| 0.9949 \|
	\| 0.0006 \| 26.0 \| 7280 \| 0.0408 \| 0.9819 \| 0.9802 \| 0.9811 \| 0.9940 \|
	\| 0.0005 \| 27.0 \| 7560 \| 0.0357 \| 0.9867 \| 0.9826 \| 0.9846 \| 0.9953 \|
	\| 0.0005 \| 28.0 \| 7840 \| 0.0370 \| 0.9843 \| 0.9820 \| 0.9832 \| 0.9946 \|
	\| 0.0004 \| 29.0 \| 8120 \| 0.0313 \| 0.9880 \| 0.9874 \| 0.9877 \| 0.9960 \|
	\| 0.0004 \| 30.0 \| 8400 \| 0.0363 \| 0.9892 \| 0.9862 \| 0.9877 \| 0.9956 \|
	\| 0.0004 \| 31.0 \| 8680 \| 0.0402 \| 0.9843 \| 0.9826 \| 0.9835 \| 0.9946 \|
	\| 0.0004 \| 32.0 \| 8960 \| 0.0321 \| 0.9868 \| 0.9850 \| 0.9859 \| 0.9956 \|
	\| 0.0004 \| 33.0 \| 9240 \| 0.0362 \| 0.9861 \| 0.9838 \| 0.9850 \| 0.9950 \|
	\| 0.0003 \| 34.0 \| 9520 \| 0.0307 \| 0.9886 \| 0.9880 \| 0.9883 \| 0.9964 \|
	\| 0.0003 \| 35.0 \| 9800 \| 0.0350 \| 0.9880 \| 0.9862 \| 0.9871 \| 0.9956 \|
	\| 0.0001 \| 36.0 \| 10080 \| 0.0343 \| 0.9868 \| 0.9856 \| 0.9862 \| 0.9956 \|
	\| 0.0001 \| 37.0 \| 10360 \| 0.0374 \| 0.9874 \| 0.9856 \| 0.9865 \| 0.9952 \|
	\| 0.0003 \| 38.0 \| 10640 \| 0.0333 \| 0.9874 \| 0.9868 \| 0.9871 \| 0.9957 \|
	\| 0.0003 \| 39.0 \| 10920 \| 0.0331 \| 0.9886 \| 0.9862 \| 0.9874 \| 0.9956 \|
	\| 0.0001 \| 40.0 \| 11200 \| 0.0349 \| 0.9880 \| 0.9868 \| 0.9874 \| 0.9961 \|
	\| 0.0001 \| 41.0 \| 11480 \| 0.0407 \| 0.9880 \| 0.9868 \| 0.9874 \| 0.9958 \|
	\| 0.0001 \| 42.0 \| 11760 \| 0.0389 \| 0.9874 \| 0.9868 \| 0.9871 \| 0.9959 \|
	\| 0.0001 \| 43.0 \| 12040 \| 0.0387 \| 0.9892 \| 0.9874 \| 0.9883 \| 0.9961 \|
	\| 0.0001 \| 44.0 \| 12320 \| 0.0414 \| 0.9886 \| 0.9868 \| 0.9877 \| 0.9959 \|
	\| 0.0001 \| 45.0 \| 12600 \| 0.0386 \| 0.9886 \| 0.9868 \| 0.9877 \| 0.9961 \|
	\| 0.0001 \| 46.0 \| 12880 \| 0.0408 \| 0.9892 \| 0.9874 \| 0.9883 \| 0.9961 \|
	\| 0.0 \| 47.0 \| 13160 \| 0.0402 \| 0.9898 \| 0.9880 \| 0.9889 \| 0.9962 \|
	\| 0.0 \| 48.0 \| 13440 \| 0.0411 \| 0.9886 \| 0.9868 \| 0.9877 \| 0.9959 \|
	\| 0.0 \| 49.0 \| 13720 \| 0.0403 \| 0.9904 \| 0.9880 \| 0.9892 \| 0.9962 \|
	\| 0.0 \| 50.0 \| 14000 \| 0.0402 \| 0.9904 \| 0.9880 \| 0.9892 \| 0.9962 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.1
	- Tokenizers 0.13.3

	---
	base_model: vinai/phobert-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: disfluency-large-3
	results: []
	---

	<!-- 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. -->

	# disfluency-large-3

	This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0403
	- Precision: 0.9904
	- Recall: 0.9880
	- F1: 0.9892
	- Accuracy: 0.9962

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.0 \| 280 \| 0.0331 \| 0.9719 \| 0.9754 \| 0.9736 \| 0.9926 \|
	\| 0.0853 \| 2.0 \| 560 \| 0.0354 \| 0.9771 \| 0.9736 \| 0.9753 \| 0.9923 \|
	\| 0.0853 \| 3.0 \| 840 \| 0.0360 \| 0.9759 \| 0.9754 \| 0.9757 \| 0.9928 \|
	\| 0.0119 \| 4.0 \| 1120 \| 0.0255 \| 0.9850 \| 0.9838 \| 0.9844 \| 0.9948 \|
	\| 0.0119 \| 5.0 \| 1400 \| 0.0300 \| 0.9873 \| 0.9850 \| 0.9862 \| 0.9952 \|
	\| 0.0063 \| 6.0 \| 1680 \| 0.0412 \| 0.9848 \| 0.9742 \| 0.9795 \| 0.9927 \|
	\| 0.0063 \| 7.0 \| 1960 \| 0.0304 \| 0.9844 \| 0.9838 \| 0.9841 \| 0.9952 \|
	\| 0.0039 \| 8.0 \| 2240 \| 0.0344 \| 0.9855 \| 0.9820 \| 0.9837 \| 0.9939 \|
	\| 0.004 \| 9.0 \| 2520 \| 0.0522 \| 0.9740 \| 0.9681 \| 0.9711 \| 0.9911 \|
	\| 0.004 \| 10.0 \| 2800 \| 0.0305 \| 0.9790 \| 0.9790 \| 0.9790 \| 0.9943 \|
	\| 0.0022 \| 11.0 \| 3080 \| 0.0355 \| 0.9837 \| 0.9820 \| 0.9829 \| 0.9945 \|
	\| 0.0022 \| 12.0 \| 3360 \| 0.0400 \| 0.9795 \| 0.9772 \| 0.9783 \| 0.9935 \|
	\| 0.002 \| 13.0 \| 3640 \| 0.0394 \| 0.9826 \| 0.9814 \| 0.9820 \| 0.9943 \|
	\| 0.002 \| 14.0 \| 3920 \| 0.0452 \| 0.9795 \| 0.9772 \| 0.9783 \| 0.9930 \|
	\| 0.0015 \| 15.0 \| 4200 \| 0.0405 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9935 \|
	\| 0.0015 \| 16.0 \| 4480 \| 0.0373 \| 0.9832 \| 0.9826 \| 0.9829 \| 0.9941 \|
	\| 0.0013 \| 17.0 \| 4760 \| 0.0361 \| 0.9832 \| 0.9850 \| 0.9841 \| 0.9946 \|
	\| 0.0013 \| 18.0 \| 5040 \| 0.0447 \| 0.9807 \| 0.9790 \| 0.9798 \| 0.9937 \|
	\| 0.0013 \| 19.0 \| 5320 \| 0.0340 \| 0.9874 \| 0.9856 \| 0.9865 \| 0.9955 \|
	\| 0.0009 \| 20.0 \| 5600 \| 0.0374 \| 0.9873 \| 0.9826 \| 0.9849 \| 0.9948 \|
	\| 0.0009 \| 21.0 \| 5880 \| 0.0410 \| 0.9843 \| 0.9784 \| 0.9813 \| 0.9943 \|
	\| 0.0007 \| 22.0 \| 6160 \| 0.0275 \| 0.9892 \| 0.9862 \| 0.9877 \| 0.9961 \|
	\| 0.0007 \| 23.0 \| 6440 \| 0.0360 \| 0.9891 \| 0.9850 \| 0.9871 \| 0.9960 \|
	\| 0.0011 \| 24.0 \| 6720 \| 0.0323 \| 0.9868 \| 0.9850 \| 0.9859 \| 0.9954 \|
	\| 0.0006 \| 25.0 \| 7000 \| 0.0386 \| 0.9867 \| 0.9820 \| 0.9843 \| 0.9949 \|
	\| 0.0006 \| 26.0 \| 7280 \| 0.0408 \| 0.9819 \| 0.9802 \| 0.9811 \| 0.9940 \|
	\| 0.0005 \| 27.0 \| 7560 \| 0.0357 \| 0.9867 \| 0.9826 \| 0.9846 \| 0.9953 \|
	\| 0.0005 \| 28.0 \| 7840 \| 0.0370 \| 0.9843 \| 0.9820 \| 0.9832 \| 0.9946 \|
	\| 0.0004 \| 29.0 \| 8120 \| 0.0313 \| 0.9880 \| 0.9874 \| 0.9877 \| 0.9960 \|
	\| 0.0004 \| 30.0 \| 8400 \| 0.0363 \| 0.9892 \| 0.9862 \| 0.9877 \| 0.9956 \|
	\| 0.0004 \| 31.0 \| 8680 \| 0.0402 \| 0.9843 \| 0.9826 \| 0.9835 \| 0.9946 \|
	\| 0.0004 \| 32.0 \| 8960 \| 0.0321 \| 0.9868 \| 0.9850 \| 0.9859 \| 0.9956 \|
	\| 0.0004 \| 33.0 \| 9240 \| 0.0362 \| 0.9861 \| 0.9838 \| 0.9850 \| 0.9950 \|
	\| 0.0003 \| 34.0 \| 9520 \| 0.0307 \| 0.9886 \| 0.9880 \| 0.9883 \| 0.9964 \|
	\| 0.0003 \| 35.0 \| 9800 \| 0.0350 \| 0.9880 \| 0.9862 \| 0.9871 \| 0.9956 \|
	\| 0.0001 \| 36.0 \| 10080 \| 0.0343 \| 0.9868 \| 0.9856 \| 0.9862 \| 0.9956 \|
	\| 0.0001 \| 37.0 \| 10360 \| 0.0374 \| 0.9874 \| 0.9856 \| 0.9865 \| 0.9952 \|
	\| 0.0003 \| 38.0 \| 10640 \| 0.0333 \| 0.9874 \| 0.9868 \| 0.9871 \| 0.9957 \|
	\| 0.0003 \| 39.0 \| 10920 \| 0.0331 \| 0.9886 \| 0.9862 \| 0.9874 \| 0.9956 \|
	\| 0.0001 \| 40.0 \| 11200 \| 0.0349 \| 0.9880 \| 0.9868 \| 0.9874 \| 0.9961 \|
	\| 0.0001 \| 41.0 \| 11480 \| 0.0407 \| 0.9880 \| 0.9868 \| 0.9874 \| 0.9958 \|
	\| 0.0001 \| 42.0 \| 11760 \| 0.0389 \| 0.9874 \| 0.9868 \| 0.9871 \| 0.9959 \|
	\| 0.0001 \| 43.0 \| 12040 \| 0.0387 \| 0.9892 \| 0.9874 \| 0.9883 \| 0.9961 \|
	\| 0.0001 \| 44.0 \| 12320 \| 0.0414 \| 0.9886 \| 0.9868 \| 0.9877 \| 0.9959 \|
	\| 0.0001 \| 45.0 \| 12600 \| 0.0386 \| 0.9886 \| 0.9868 \| 0.9877 \| 0.9961 \|
	\| 0.0001 \| 46.0 \| 12880 \| 0.0408 \| 0.9892 \| 0.9874 \| 0.9883 \| 0.9961 \|
	\| 0.0 \| 47.0 \| 13160 \| 0.0402 \| 0.9898 \| 0.9880 \| 0.9889 \| 0.9962 \|
	\| 0.0 \| 48.0 \| 13440 \| 0.0411 \| 0.9886 \| 0.9868 \| 0.9877 \| 0.9959 \|
	\| 0.0 \| 49.0 \| 13720 \| 0.0403 \| 0.9904 \| 0.9880 \| 0.9892 \| 0.9962 \|
	\| 0.0 \| 50.0 \| 14000 \| 0.0402 \| 0.9904 \| 0.9880 \| 0.9892 \| 0.9962 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.1
	- Tokenizers 0.13.3