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	---
	base_model: microsoft/mdeberta-v3-base
	library_name: transformers
	license: mit
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	tags:
	- generated_from_trainer
	model-index:
	- name: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full44
	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. -->

	# scenario-non-kd-scr-ner-half-mdeberta_data-univner_full44

	This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3028
	- Precision: 0.6277
	- Recall: 0.5869
	- F1: 0.6066
	- Accuracy: 0.9615

	## 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: 3e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 44
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.36 \| 0.2910 \| 500 \| 0.2872 \| 0.2875 \| 0.1173 \| 0.1666 \| 0.9284 \|
	\| 0.2389 \| 0.5821 \| 1000 \| 0.2086 \| 0.3476 \| 0.2561 \| 0.2949 \| 0.9388 \|
	\| 0.1727 \| 0.8731 \| 1500 \| 0.1810 \| 0.4363 \| 0.3748 \| 0.4033 \| 0.9465 \|
	\| 0.1338 \| 1.1641 \| 2000 \| 0.1644 \| 0.4626 \| 0.4675 \| 0.4650 \| 0.9514 \|
	\| 0.1122 \| 1.4552 \| 2500 \| 0.1560 \| 0.4983 \| 0.5063 \| 0.5023 \| 0.9538 \|
	\| 0.1033 \| 1.7462 \| 3000 \| 0.1504 \| 0.5354 \| 0.5128 \| 0.5238 \| 0.9564 \|
	\| 0.0919 \| 2.0373 \| 3500 \| 0.1475 \| 0.5073 \| 0.5452 \| 0.5256 \| 0.9558 \|
	\| 0.0665 \| 2.3283 \| 4000 \| 0.1547 \| 0.5536 \| 0.5578 \| 0.5557 \| 0.9583 \|
	\| 0.066 \| 2.6193 \| 4500 \| 0.1513 \| 0.5345 \| 0.5760 \| 0.5545 \| 0.9581 \|
	\| 0.0642 \| 2.9104 \| 5000 \| 0.1489 \| 0.5750 \| 0.5683 \| 0.5716 \| 0.9605 \|
	\| 0.0495 \| 3.2014 \| 5500 \| 0.1597 \| 0.5769 \| 0.5711 \| 0.5740 \| 0.9600 \|
	\| 0.0437 \| 3.4924 \| 6000 \| 0.1643 \| 0.5848 \| 0.5680 \| 0.5763 \| 0.9603 \|
	\| 0.0444 \| 3.7835 \| 6500 \| 0.1615 \| 0.5884 \| 0.5898 \| 0.5891 \| 0.9607 \|
	\| 0.0409 \| 4.0745 \| 7000 \| 0.1723 \| 0.5869 \| 0.5761 \| 0.5815 \| 0.9606 \|
	\| 0.0313 \| 4.3655 \| 7500 \| 0.1740 \| 0.5871 \| 0.5930 \| 0.5900 \| 0.9606 \|
	\| 0.032 \| 4.6566 \| 8000 \| 0.1682 \| 0.5911 \| 0.6031 \| 0.5971 \| 0.9611 \|
	\| 0.0304 \| 4.9476 \| 8500 \| 0.1771 \| 0.6070 \| 0.5783 \| 0.5923 \| 0.9613 \|
	\| 0.0228 \| 5.2386 \| 9000 \| 0.1843 \| 0.5817 \| 0.6045 \| 0.5929 \| 0.9608 \|
	\| 0.0216 \| 5.5297 \| 9500 \| 0.1841 \| 0.5938 \| 0.6142 \| 0.6038 \| 0.9609 \|
	\| 0.0232 \| 5.8207 \| 10000 \| 0.1957 \| 0.5816 \| 0.5998 \| 0.5906 \| 0.9600 \|
	\| 0.0201 \| 6.1118 \| 10500 \| 0.1982 \| 0.6049 \| 0.5963 \| 0.6006 \| 0.9611 \|
	\| 0.0153 \| 6.4028 \| 11000 \| 0.2040 \| 0.5919 \| 0.6057 \| 0.5987 \| 0.9602 \|
	\| 0.0165 \| 6.6938 \| 11500 \| 0.2039 \| 0.6000 \| 0.5988 \| 0.5994 \| 0.9609 \|
	\| 0.0165 \| 6.9849 \| 12000 \| 0.2076 \| 0.5963 \| 0.5913 \| 0.5938 \| 0.9606 \|
	\| 0.0121 \| 7.2759 \| 12500 \| 0.2178 \| 0.6015 \| 0.5833 \| 0.5923 \| 0.9604 \|
	\| 0.012 \| 7.5669 \| 13000 \| 0.2186 \| 0.6206 \| 0.5902 \| 0.6050 \| 0.9613 \|
	\| 0.0126 \| 7.8580 \| 13500 \| 0.2218 \| 0.5882 \| 0.6191 \| 0.6033 \| 0.9600 \|
	\| 0.0098 \| 8.1490 \| 14000 \| 0.2296 \| 0.6164 \| 0.5911 \| 0.6035 \| 0.9617 \|
	\| 0.0091 \| 8.4400 \| 14500 \| 0.2332 \| 0.5986 \| 0.5976 \| 0.5981 \| 0.9607 \|
	\| 0.0097 \| 8.7311 \| 15000 \| 0.2322 \| 0.6053 \| 0.5996 \| 0.6024 \| 0.9613 \|
	\| 0.0089 \| 9.0221 \| 15500 \| 0.2355 \| 0.6174 \| 0.6034 \| 0.6103 \| 0.9612 \|
	\| 0.0064 \| 9.3132 \| 16000 \| 0.2440 \| 0.6306 \| 0.5835 \| 0.6061 \| 0.9614 \|
	\| 0.0071 \| 9.6042 \| 16500 \| 0.2451 \| 0.6220 \| 0.5761 \| 0.5982 \| 0.9609 \|
	\| 0.0073 \| 9.8952 \| 17000 \| 0.2461 \| 0.6203 \| 0.5990 \| 0.6095 \| 0.9616 \|
	\| 0.0064 \| 10.1863 \| 17500 \| 0.2506 \| 0.6213 \| 0.5900 \| 0.6052 \| 0.9615 \|
	\| 0.005 \| 10.4773 \| 18000 \| 0.2547 \| 0.6226 \| 0.5970 \| 0.6096 \| 0.9617 \|
	\| 0.0058 \| 10.7683 \| 18500 \| 0.2553 \| 0.6374 \| 0.5897 \| 0.6126 \| 0.9620 \|
	\| 0.0054 \| 11.0594 \| 19000 \| 0.2624 \| 0.6232 \| 0.5840 \| 0.6030 \| 0.9617 \|
	\| 0.0044 \| 11.3504 \| 19500 \| 0.2655 \| 0.6262 \| 0.5946 \| 0.6100 \| 0.9620 \|
	\| 0.0048 \| 11.6414 \| 20000 \| 0.2654 \| 0.6154 \| 0.5989 \| 0.6070 \| 0.9616 \|
	\| 0.0042 \| 11.9325 \| 20500 \| 0.2724 \| 0.6306 \| 0.5806 \| 0.6046 \| 0.9616 \|
	\| 0.004 \| 12.2235 \| 21000 \| 0.2707 \| 0.6052 \| 0.5920 \| 0.5985 \| 0.9607 \|
	\| 0.0035 \| 12.5146 \| 21500 \| 0.2714 \| 0.5962 \| 0.5986 \| 0.5974 \| 0.9607 \|
	\| 0.0041 \| 12.8056 \| 22000 \| 0.2755 \| 0.6263 \| 0.5858 \| 0.6053 \| 0.9616 \|
	\| 0.0035 \| 13.0966 \| 22500 \| 0.2842 \| 0.6350 \| 0.5814 \| 0.6071 \| 0.9614 \|
	\| 0.0033 \| 13.3877 \| 23000 \| 0.2763 \| 0.6317 \| 0.5868 \| 0.6084 \| 0.9614 \|
	\| 0.0028 \| 13.6787 \| 23500 \| 0.2831 \| 0.6141 \| 0.5976 \| 0.6057 \| 0.9616 \|
	\| 0.0031 \| 13.9697 \| 24000 \| 0.2797 \| 0.6141 \| 0.6064 \| 0.6102 \| 0.9614 \|
	\| 0.0024 \| 14.2608 \| 24500 \| 0.2873 \| 0.5980 \| 0.6038 \| 0.6009 \| 0.9611 \|
	\| 0.0025 \| 14.5518 \| 25000 \| 0.2913 \| 0.6055 \| 0.5980 \| 0.6017 \| 0.9612 \|
	\| 0.003 \| 14.8428 \| 25500 \| 0.2885 \| 0.6208 \| 0.5843 \| 0.6020 \| 0.9615 \|
	\| 0.0023 \| 15.1339 \| 26000 \| 0.2923 \| 0.6255 \| 0.5849 \| 0.6045 \| 0.9618 \|
	\| 0.0019 \| 15.4249 \| 26500 \| 0.2875 \| 0.6221 \| 0.6015 \| 0.6116 \| 0.9619 \|
	\| 0.0027 \| 15.7159 \| 27000 \| 0.2898 \| 0.6241 \| 0.5967 \| 0.6101 \| 0.9619 \|
	\| 0.0024 \| 16.0070 \| 27500 \| 0.2943 \| 0.6146 \| 0.5895 \| 0.6018 \| 0.9612 \|
	\| 0.0016 \| 16.2980 \| 28000 \| 0.2996 \| 0.6199 \| 0.5928 \| 0.6060 \| 0.9614 \|
	\| 0.0022 \| 16.5891 \| 28500 \| 0.3028 \| 0.6277 \| 0.5869 \| 0.6066 \| 0.9615 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.1.1+cu121
	- Datasets 2.14.5
	- Tokenizers 0.19.1

	---
	base_model: microsoft/mdeberta-v3-base
	library_name: transformers
	license: mit
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	tags:
	- generated_from_trainer
	model-index:
	- name: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full44
	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. -->

	# scenario-non-kd-scr-ner-half-mdeberta_data-univner_full44

	This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3028
	- Precision: 0.6277
	- Recall: 0.5869
	- F1: 0.6066
	- Accuracy: 0.9615

	## 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: 3e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 44
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.36 \| 0.2910 \| 500 \| 0.2872 \| 0.2875 \| 0.1173 \| 0.1666 \| 0.9284 \|
	\| 0.2389 \| 0.5821 \| 1000 \| 0.2086 \| 0.3476 \| 0.2561 \| 0.2949 \| 0.9388 \|
	\| 0.1727 \| 0.8731 \| 1500 \| 0.1810 \| 0.4363 \| 0.3748 \| 0.4033 \| 0.9465 \|
	\| 0.1338 \| 1.1641 \| 2000 \| 0.1644 \| 0.4626 \| 0.4675 \| 0.4650 \| 0.9514 \|
	\| 0.1122 \| 1.4552 \| 2500 \| 0.1560 \| 0.4983 \| 0.5063 \| 0.5023 \| 0.9538 \|
	\| 0.1033 \| 1.7462 \| 3000 \| 0.1504 \| 0.5354 \| 0.5128 \| 0.5238 \| 0.9564 \|
	\| 0.0919 \| 2.0373 \| 3500 \| 0.1475 \| 0.5073 \| 0.5452 \| 0.5256 \| 0.9558 \|
	\| 0.0665 \| 2.3283 \| 4000 \| 0.1547 \| 0.5536 \| 0.5578 \| 0.5557 \| 0.9583 \|
	\| 0.066 \| 2.6193 \| 4500 \| 0.1513 \| 0.5345 \| 0.5760 \| 0.5545 \| 0.9581 \|
	\| 0.0642 \| 2.9104 \| 5000 \| 0.1489 \| 0.5750 \| 0.5683 \| 0.5716 \| 0.9605 \|
	\| 0.0495 \| 3.2014 \| 5500 \| 0.1597 \| 0.5769 \| 0.5711 \| 0.5740 \| 0.9600 \|
	\| 0.0437 \| 3.4924 \| 6000 \| 0.1643 \| 0.5848 \| 0.5680 \| 0.5763 \| 0.9603 \|
	\| 0.0444 \| 3.7835 \| 6500 \| 0.1615 \| 0.5884 \| 0.5898 \| 0.5891 \| 0.9607 \|
	\| 0.0409 \| 4.0745 \| 7000 \| 0.1723 \| 0.5869 \| 0.5761 \| 0.5815 \| 0.9606 \|
	\| 0.0313 \| 4.3655 \| 7500 \| 0.1740 \| 0.5871 \| 0.5930 \| 0.5900 \| 0.9606 \|
	\| 0.032 \| 4.6566 \| 8000 \| 0.1682 \| 0.5911 \| 0.6031 \| 0.5971 \| 0.9611 \|
	\| 0.0304 \| 4.9476 \| 8500 \| 0.1771 \| 0.6070 \| 0.5783 \| 0.5923 \| 0.9613 \|
	\| 0.0228 \| 5.2386 \| 9000 \| 0.1843 \| 0.5817 \| 0.6045 \| 0.5929 \| 0.9608 \|
	\| 0.0216 \| 5.5297 \| 9500 \| 0.1841 \| 0.5938 \| 0.6142 \| 0.6038 \| 0.9609 \|
	\| 0.0232 \| 5.8207 \| 10000 \| 0.1957 \| 0.5816 \| 0.5998 \| 0.5906 \| 0.9600 \|
	\| 0.0201 \| 6.1118 \| 10500 \| 0.1982 \| 0.6049 \| 0.5963 \| 0.6006 \| 0.9611 \|
	\| 0.0153 \| 6.4028 \| 11000 \| 0.2040 \| 0.5919 \| 0.6057 \| 0.5987 \| 0.9602 \|
	\| 0.0165 \| 6.6938 \| 11500 \| 0.2039 \| 0.6000 \| 0.5988 \| 0.5994 \| 0.9609 \|
	\| 0.0165 \| 6.9849 \| 12000 \| 0.2076 \| 0.5963 \| 0.5913 \| 0.5938 \| 0.9606 \|
	\| 0.0121 \| 7.2759 \| 12500 \| 0.2178 \| 0.6015 \| 0.5833 \| 0.5923 \| 0.9604 \|
	\| 0.012 \| 7.5669 \| 13000 \| 0.2186 \| 0.6206 \| 0.5902 \| 0.6050 \| 0.9613 \|
	\| 0.0126 \| 7.8580 \| 13500 \| 0.2218 \| 0.5882 \| 0.6191 \| 0.6033 \| 0.9600 \|
	\| 0.0098 \| 8.1490 \| 14000 \| 0.2296 \| 0.6164 \| 0.5911 \| 0.6035 \| 0.9617 \|
	\| 0.0091 \| 8.4400 \| 14500 \| 0.2332 \| 0.5986 \| 0.5976 \| 0.5981 \| 0.9607 \|
	\| 0.0097 \| 8.7311 \| 15000 \| 0.2322 \| 0.6053 \| 0.5996 \| 0.6024 \| 0.9613 \|
	\| 0.0089 \| 9.0221 \| 15500 \| 0.2355 \| 0.6174 \| 0.6034 \| 0.6103 \| 0.9612 \|
	\| 0.0064 \| 9.3132 \| 16000 \| 0.2440 \| 0.6306 \| 0.5835 \| 0.6061 \| 0.9614 \|
	\| 0.0071 \| 9.6042 \| 16500 \| 0.2451 \| 0.6220 \| 0.5761 \| 0.5982 \| 0.9609 \|
	\| 0.0073 \| 9.8952 \| 17000 \| 0.2461 \| 0.6203 \| 0.5990 \| 0.6095 \| 0.9616 \|
	\| 0.0064 \| 10.1863 \| 17500 \| 0.2506 \| 0.6213 \| 0.5900 \| 0.6052 \| 0.9615 \|
	\| 0.005 \| 10.4773 \| 18000 \| 0.2547 \| 0.6226 \| 0.5970 \| 0.6096 \| 0.9617 \|
	\| 0.0058 \| 10.7683 \| 18500 \| 0.2553 \| 0.6374 \| 0.5897 \| 0.6126 \| 0.9620 \|
	\| 0.0054 \| 11.0594 \| 19000 \| 0.2624 \| 0.6232 \| 0.5840 \| 0.6030 \| 0.9617 \|
	\| 0.0044 \| 11.3504 \| 19500 \| 0.2655 \| 0.6262 \| 0.5946 \| 0.6100 \| 0.9620 \|
	\| 0.0048 \| 11.6414 \| 20000 \| 0.2654 \| 0.6154 \| 0.5989 \| 0.6070 \| 0.9616 \|
	\| 0.0042 \| 11.9325 \| 20500 \| 0.2724 \| 0.6306 \| 0.5806 \| 0.6046 \| 0.9616 \|
	\| 0.004 \| 12.2235 \| 21000 \| 0.2707 \| 0.6052 \| 0.5920 \| 0.5985 \| 0.9607 \|
	\| 0.0035 \| 12.5146 \| 21500 \| 0.2714 \| 0.5962 \| 0.5986 \| 0.5974 \| 0.9607 \|
	\| 0.0041 \| 12.8056 \| 22000 \| 0.2755 \| 0.6263 \| 0.5858 \| 0.6053 \| 0.9616 \|
	\| 0.0035 \| 13.0966 \| 22500 \| 0.2842 \| 0.6350 \| 0.5814 \| 0.6071 \| 0.9614 \|
	\| 0.0033 \| 13.3877 \| 23000 \| 0.2763 \| 0.6317 \| 0.5868 \| 0.6084 \| 0.9614 \|
	\| 0.0028 \| 13.6787 \| 23500 \| 0.2831 \| 0.6141 \| 0.5976 \| 0.6057 \| 0.9616 \|
	\| 0.0031 \| 13.9697 \| 24000 \| 0.2797 \| 0.6141 \| 0.6064 \| 0.6102 \| 0.9614 \|
	\| 0.0024 \| 14.2608 \| 24500 \| 0.2873 \| 0.5980 \| 0.6038 \| 0.6009 \| 0.9611 \|
	\| 0.0025 \| 14.5518 \| 25000 \| 0.2913 \| 0.6055 \| 0.5980 \| 0.6017 \| 0.9612 \|
	\| 0.003 \| 14.8428 \| 25500 \| 0.2885 \| 0.6208 \| 0.5843 \| 0.6020 \| 0.9615 \|
	\| 0.0023 \| 15.1339 \| 26000 \| 0.2923 \| 0.6255 \| 0.5849 \| 0.6045 \| 0.9618 \|
	\| 0.0019 \| 15.4249 \| 26500 \| 0.2875 \| 0.6221 \| 0.6015 \| 0.6116 \| 0.9619 \|
	\| 0.0027 \| 15.7159 \| 27000 \| 0.2898 \| 0.6241 \| 0.5967 \| 0.6101 \| 0.9619 \|
	\| 0.0024 \| 16.0070 \| 27500 \| 0.2943 \| 0.6146 \| 0.5895 \| 0.6018 \| 0.9612 \|
	\| 0.0016 \| 16.2980 \| 28000 \| 0.2996 \| 0.6199 \| 0.5928 \| 0.6060 \| 0.9614 \|
	\| 0.0022 \| 16.5891 \| 28500 \| 0.3028 \| 0.6277 \| 0.5869 \| 0.6066 \| 0.9615 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.1.1+cu121
	- Datasets 2.14.5
	- Tokenizers 0.19.1