MedMobile / README.md

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	---
	pipeline_tag: text-generation
	license: mit
	base_model: microsoft/Phi-3-mini-4k-instruct
	tags:
	- trl
	- sft
	- generated_from_trainer
	model-index:
	- name: MedMobile
	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. -->

	# MedMobile

	Manuscript: https://arxiv.org/abs/2410.09019

	This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the UltraMedical dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7358

	## 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: 0.0001
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 0.8656 \| 0.0225 \| 200 \| 0.7711 \|
	\| 0.7615 \| 0.0451 \| 400 \| 0.7521 \|
	\| 0.748 \| 0.0676 \| 600 \| 0.7457 \|
	\| 0.7465 \| 0.0902 \| 800 \| 0.7428 \|
	\| 0.7468 \| 0.1127 \| 1000 \| 0.7419 \|
	\| 0.7434 \| 0.1352 \| 1200 \| 0.7429 \|
	\| 0.7467 \| 0.1578 \| 1400 \| 0.7451 \|
	\| 0.7508 \| 0.1803 \| 1600 \| 0.7469 \|
	\| 0.7505 \| 0.2029 \| 1800 \| 0.7503 \|
	\| 0.7541 \| 0.2254 \| 2000 \| 0.7531 \|
	\| 0.7559 \| 0.2479 \| 2200 \| 0.7576 \|
	\| 0.7592 \| 0.2705 \| 2400 \| 0.7599 \|
	\| 0.7729 \| 0.2930 \| 2600 \| 0.7635 \|
	\| 0.772 \| 0.3156 \| 2800 \| 0.7645 \|
	\| 0.7707 \| 0.3381 \| 3000 \| 0.7628 \|
	\| 0.7616 \| 0.3606 \| 3200 \| 0.7614 \|
	\| 0.7632 \| 0.3832 \| 3400 \| 0.7590 \|
	\| 0.7613 \| 0.4057 \| 3600 \| 0.7574 \|
	\| 0.7581 \| 0.4283 \| 3800 \| 0.7558 \|
	\| 0.7583 \| 0.4508 \| 4000 \| 0.7539 \|
	\| 0.7509 \| 0.4733 \| 4200 \| 0.7518 \|
	\| 0.7559 \| 0.4959 \| 4400 \| 0.7506 \|
	\| 0.7523 \| 0.5184 \| 4600 \| 0.7491 \|
	\| 0.7461 \| 0.5410 \| 4800 \| 0.7469 \|
	\| 0.7504 \| 0.5635 \| 5000 \| 0.7464 \|
	\| 0.7486 \| 0.5860 \| 5200 \| 0.7449 \|
	\| 0.7454 \| 0.6086 \| 5400 \| 0.7436 \|
	\| 0.7451 \| 0.6311 \| 5600 \| 0.7427 \|
	\| 0.7431 \| 0.6537 \| 5800 \| 0.7412 \|
	\| 0.7438 \| 0.6762 \| 6000 \| 0.7402 \|
	\| 0.7471 \| 0.6987 \| 6200 \| 0.7390 \|
	\| 0.7416 \| 0.7213 \| 6400 \| 0.7378 \|
	\| 0.7345 \| 0.7438 \| 6600 \| 0.7364 \|
	\| 0.7437 \| 0.7663 \| 6800 \| 0.7349 \|
	\| 0.7431 \| 0.7889 \| 7000 \| 0.7349 \|
	\| 0.737 \| 0.8114 \| 7200 \| 0.7339 \|
	\| 0.7358 \| 0.8340 \| 7400 \| 0.7333 \|
	\| 0.7336 \| 0.8565 \| 7600 \| 0.7320 \|
	\| 0.7327 \| 0.8790 \| 7800 \| 0.7310 \|
	\| 0.7288 \| 0.9016 \| 8000 \| 0.7303 \|
	\| 0.7326 \| 0.9241 \| 8200 \| 0.7295 \|
	\| 0.7354 \| 0.9467 \| 8400 \| 0.7287 \|
	\| 0.731 \| 0.9692 \| 8600 \| 0.7278 \|
	\| 0.7317 \| 0.9917 \| 8800 \| 0.7272 \|
	\| 0.6809 \| 1.0143 \| 9000 \| 0.7359 \|
	\| 0.6548 \| 1.0368 \| 9200 \| 0.7341 \|
	\| 0.6463 \| 1.0594 \| 9400 \| 0.7353 \|
	\| 0.6516 \| 1.0819 \| 9600 \| 0.7357 \|
	\| 0.6544 \| 1.1044 \| 9800 \| 0.7345 \|
	\| 0.6558 \| 1.1270 \| 10000 \| 0.7342 \|
	\| 0.6532 \| 1.1495 \| 10200 \| 0.7331 \|
	\| 0.653 \| 1.1721 \| 10400 \| 0.7328 \|
	\| 0.6583 \| 1.1946 \| 10600 \| 0.7323 \|
	\| 0.6537 \| 1.2171 \| 10800 \| 0.7326 \|
	\| 0.6622 \| 1.2397 \| 11000 \| 0.7318 \|
	\| 0.6596 \| 1.2622 \| 11200 \| 0.7315 \|
	\| 0.6522 \| 1.2848 \| 11400 \| 0.7304 \|
	\| 0.6517 \| 1.3073 \| 11600 \| 0.7300 \|
	\| 0.657 \| 1.3298 \| 11800 \| 0.7296 \|
	\| 0.6554 \| 1.3524 \| 12000 \| 0.7286 \|
	\| 0.6545 \| 1.3749 \| 12200 \| 0.7287 \|
	\| 0.6556 \| 1.3975 \| 12400 \| 0.7283 \|
	\| 0.655 \| 1.4200 \| 12600 \| 0.7294 \|
	\| 0.6489 \| 1.4425 \| 12800 \| 0.7285 \|
	\| 0.6539 \| 1.4651 \| 13000 \| 0.7269 \|
	\| 0.654 \| 1.4876 \| 13200 \| 0.7273 \|
	\| 0.6556 \| 1.5102 \| 13400 \| 0.7273 \|
	\| 0.6529 \| 1.5327 \| 13600 \| 0.7271 \|
	\| 0.6504 \| 1.5552 \| 13800 \| 0.7264 \|
	\| 0.6498 \| 1.5778 \| 14000 \| 0.7256 \|
	\| 0.6517 \| 1.6003 \| 14200 \| 0.7255 \|
	\| 0.656 \| 1.6229 \| 14400 \| 0.7252 \|
	\| 0.6471 \| 1.6454 \| 14600 \| 0.7242 \|
	\| 0.6485 \| 1.6679 \| 14800 \| 0.7243 \|
	\| 0.6545 \| 1.6905 \| 15000 \| 0.7242 \|
	\| 0.6527 \| 1.7130 \| 15200 \| 0.7238 \|
	\| 0.6504 \| 1.7356 \| 15400 \| 0.7236 \|
	\| 0.6492 \| 1.7581 \| 15600 \| 0.7229 \|
	\| 0.6529 \| 1.7806 \| 15800 \| 0.7232 \|
	\| 0.6507 \| 1.8032 \| 16000 \| 0.7226 \|
	\| 0.653 \| 1.8257 \| 16200 \| 0.7229 \|
	\| 0.6461 \| 1.8483 \| 16400 \| 0.7223 \|
	\| 0.6453 \| 1.8708 \| 16600 \| 0.7221 \|
	\| 0.6534 \| 1.8933 \| 16800 \| 0.7219 \|
	\| 0.6455 \| 1.9159 \| 17000 \| 0.7220 \|
	\| 0.6485 \| 1.9384 \| 17200 \| 0.7212 \|
	\| 0.6536 \| 1.9610 \| 17400 \| 0.7214 \|
	\| 0.6444 \| 1.9835 \| 17600 \| 0.7211 \|
	\| 0.6346 \| 2.0060 \| 17800 \| 0.7356 \|
	\| 0.5929 \| 2.0286 \| 18000 \| 0.7368 \|
	\| 0.5951 \| 2.0511 \| 18200 \| 0.7371 \|
	\| 0.6013 \| 2.0736 \| 18400 \| 0.7374 \|
	\| 0.6004 \| 2.0962 \| 18600 \| 0.7375 \|
	\| 0.5991 \| 2.1187 \| 18800 \| 0.7375 \|
	\| 0.5971 \| 2.1413 \| 19000 \| 0.7369 \|
	\| 0.597 \| 2.1638 \| 19200 \| 0.7380 \|
	\| 0.5951 \| 2.1863 \| 19400 \| 0.7370 \|
	\| 0.5916 \| 2.2089 \| 19600 \| 0.7370 \|
	\| 0.5992 \| 2.2314 \| 19800 \| 0.7372 \|
	\| 0.6011 \| 2.2540 \| 20000 \| 0.7364 \|
	\| 0.6003 \| 2.2765 \| 20200 \| 0.7370 \|
	\| 0.6003 \| 2.2990 \| 20400 \| 0.7370 \|
	\| 0.5985 \| 2.3216 \| 20600 \| 0.7370 \|
	\| 0.5988 \| 2.3441 \| 20800 \| 0.7367 \|
	\| 0.5959 \| 2.3667 \| 21000 \| 0.7370 \|
	\| 0.6019 \| 2.3892 \| 21200 \| 0.7370 \|
	\| 0.5977 \| 2.4117 \| 21400 \| 0.7367 \|
	\| 0.602 \| 2.4343 \| 21600 \| 0.7368 \|
	\| 0.5958 \| 2.4568 \| 21800 \| 0.7368 \|
	\| 0.5969 \| 2.4794 \| 22000 \| 0.7360 \|
	\| 0.6025 \| 2.5019 \| 22200 \| 0.7362 \|
	\| 0.5942 \| 2.5244 \| 22400 \| 0.7361 \|
	\| 0.6006 \| 2.5470 \| 22600 \| 0.7361 \|
	\| 0.5952 \| 2.5695 \| 22800 \| 0.7366 \|
	\| 0.6007 \| 2.5921 \| 23000 \| 0.7363 \|
	\| 0.6003 \| 2.6146 \| 23200 \| 0.7363 \|
	\| 0.6006 \| 2.6371 \| 23400 \| 0.7359 \|
	\| 0.6014 \| 2.6597 \| 23600 \| 0.7360 \|
	\| 0.6008 \| 2.6822 \| 23800 \| 0.7356 \|
	\| 0.6005 \| 2.7048 \| 24000 \| 0.7357 \|
	\| 0.5958 \| 2.7273 \| 24200 \| 0.7356 \|
	\| 0.5977 \| 2.7498 \| 24400 \| 0.7358 \|
	\| 0.6 \| 2.7724 \| 24600 \| 0.7358 \|
	\| 0.5978 \| 2.7949 \| 24800 \| 0.7362 \|
	\| 0.6018 \| 2.8175 \| 25000 \| 0.7359 \|
	\| 0.6079 \| 2.8400 \| 25200 \| 0.7359 \|
	\| 0.6036 \| 2.8625 \| 25400 \| 0.7359 \|
	\| 0.5985 \| 2.8851 \| 25600 \| 0.7359 \|
	\| 0.6019 \| 2.9076 \| 25800 \| 0.7359 \|
	\| 0.5994 \| 2.9302 \| 26000 \| 0.7358 \|
	\| 0.6027 \| 2.9527 \| 26200 \| 0.7358 \|
	\| 0.6014 \| 2.9752 \| 26400 \| 0.7358 \|
	\| 0.5957 \| 2.9978 \| 26600 \| 0.7358 \|


	### Framework versions

	- Transformers 4.43.3
	- Pytorch 2.3.1+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1

	---
	pipeline_tag: text-generation
	license: mit
	base_model: microsoft/Phi-3-mini-4k-instruct
	tags:
	- trl
	- sft
	- generated_from_trainer
	model-index:
	- name: MedMobile
	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. -->

	# MedMobile

	Manuscript: https://arxiv.org/abs/2410.09019

	This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the UltraMedical dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7358

	## 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: 0.0001
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 0.8656 \| 0.0225 \| 200 \| 0.7711 \|
	\| 0.7615 \| 0.0451 \| 400 \| 0.7521 \|
	\| 0.748 \| 0.0676 \| 600 \| 0.7457 \|
	\| 0.7465 \| 0.0902 \| 800 \| 0.7428 \|
	\| 0.7468 \| 0.1127 \| 1000 \| 0.7419 \|
	\| 0.7434 \| 0.1352 \| 1200 \| 0.7429 \|
	\| 0.7467 \| 0.1578 \| 1400 \| 0.7451 \|
	\| 0.7508 \| 0.1803 \| 1600 \| 0.7469 \|
	\| 0.7505 \| 0.2029 \| 1800 \| 0.7503 \|
	\| 0.7541 \| 0.2254 \| 2000 \| 0.7531 \|
	\| 0.7559 \| 0.2479 \| 2200 \| 0.7576 \|
	\| 0.7592 \| 0.2705 \| 2400 \| 0.7599 \|
	\| 0.7729 \| 0.2930 \| 2600 \| 0.7635 \|
	\| 0.772 \| 0.3156 \| 2800 \| 0.7645 \|
	\| 0.7707 \| 0.3381 \| 3000 \| 0.7628 \|
	\| 0.7616 \| 0.3606 \| 3200 \| 0.7614 \|
	\| 0.7632 \| 0.3832 \| 3400 \| 0.7590 \|
	\| 0.7613 \| 0.4057 \| 3600 \| 0.7574 \|
	\| 0.7581 \| 0.4283 \| 3800 \| 0.7558 \|
	\| 0.7583 \| 0.4508 \| 4000 \| 0.7539 \|
	\| 0.7509 \| 0.4733 \| 4200 \| 0.7518 \|
	\| 0.7559 \| 0.4959 \| 4400 \| 0.7506 \|
	\| 0.7523 \| 0.5184 \| 4600 \| 0.7491 \|
	\| 0.7461 \| 0.5410 \| 4800 \| 0.7469 \|
	\| 0.7504 \| 0.5635 \| 5000 \| 0.7464 \|
	\| 0.7486 \| 0.5860 \| 5200 \| 0.7449 \|
	\| 0.7454 \| 0.6086 \| 5400 \| 0.7436 \|
	\| 0.7451 \| 0.6311 \| 5600 \| 0.7427 \|
	\| 0.7431 \| 0.6537 \| 5800 \| 0.7412 \|
	\| 0.7438 \| 0.6762 \| 6000 \| 0.7402 \|
	\| 0.7471 \| 0.6987 \| 6200 \| 0.7390 \|
	\| 0.7416 \| 0.7213 \| 6400 \| 0.7378 \|
	\| 0.7345 \| 0.7438 \| 6600 \| 0.7364 \|
	\| 0.7437 \| 0.7663 \| 6800 \| 0.7349 \|
	\| 0.7431 \| 0.7889 \| 7000 \| 0.7349 \|
	\| 0.737 \| 0.8114 \| 7200 \| 0.7339 \|
	\| 0.7358 \| 0.8340 \| 7400 \| 0.7333 \|
	\| 0.7336 \| 0.8565 \| 7600 \| 0.7320 \|
	\| 0.7327 \| 0.8790 \| 7800 \| 0.7310 \|
	\| 0.7288 \| 0.9016 \| 8000 \| 0.7303 \|
	\| 0.7326 \| 0.9241 \| 8200 \| 0.7295 \|
	\| 0.7354 \| 0.9467 \| 8400 \| 0.7287 \|
	\| 0.731 \| 0.9692 \| 8600 \| 0.7278 \|
	\| 0.7317 \| 0.9917 \| 8800 \| 0.7272 \|
	\| 0.6809 \| 1.0143 \| 9000 \| 0.7359 \|
	\| 0.6548 \| 1.0368 \| 9200 \| 0.7341 \|
	\| 0.6463 \| 1.0594 \| 9400 \| 0.7353 \|
	\| 0.6516 \| 1.0819 \| 9600 \| 0.7357 \|
	\| 0.6544 \| 1.1044 \| 9800 \| 0.7345 \|
	\| 0.6558 \| 1.1270 \| 10000 \| 0.7342 \|
	\| 0.6532 \| 1.1495 \| 10200 \| 0.7331 \|
	\| 0.653 \| 1.1721 \| 10400 \| 0.7328 \|
	\| 0.6583 \| 1.1946 \| 10600 \| 0.7323 \|
	\| 0.6537 \| 1.2171 \| 10800 \| 0.7326 \|
	\| 0.6622 \| 1.2397 \| 11000 \| 0.7318 \|
	\| 0.6596 \| 1.2622 \| 11200 \| 0.7315 \|
	\| 0.6522 \| 1.2848 \| 11400 \| 0.7304 \|
	\| 0.6517 \| 1.3073 \| 11600 \| 0.7300 \|
	\| 0.657 \| 1.3298 \| 11800 \| 0.7296 \|
	\| 0.6554 \| 1.3524 \| 12000 \| 0.7286 \|
	\| 0.6545 \| 1.3749 \| 12200 \| 0.7287 \|
	\| 0.6556 \| 1.3975 \| 12400 \| 0.7283 \|
	\| 0.655 \| 1.4200 \| 12600 \| 0.7294 \|
	\| 0.6489 \| 1.4425 \| 12800 \| 0.7285 \|
	\| 0.6539 \| 1.4651 \| 13000 \| 0.7269 \|
	\| 0.654 \| 1.4876 \| 13200 \| 0.7273 \|
	\| 0.6556 \| 1.5102 \| 13400 \| 0.7273 \|
	\| 0.6529 \| 1.5327 \| 13600 \| 0.7271 \|
	\| 0.6504 \| 1.5552 \| 13800 \| 0.7264 \|
	\| 0.6498 \| 1.5778 \| 14000 \| 0.7256 \|
	\| 0.6517 \| 1.6003 \| 14200 \| 0.7255 \|
	\| 0.656 \| 1.6229 \| 14400 \| 0.7252 \|
	\| 0.6471 \| 1.6454 \| 14600 \| 0.7242 \|
	\| 0.6485 \| 1.6679 \| 14800 \| 0.7243 \|
	\| 0.6545 \| 1.6905 \| 15000 \| 0.7242 \|
	\| 0.6527 \| 1.7130 \| 15200 \| 0.7238 \|
	\| 0.6504 \| 1.7356 \| 15400 \| 0.7236 \|
	\| 0.6492 \| 1.7581 \| 15600 \| 0.7229 \|
	\| 0.6529 \| 1.7806 \| 15800 \| 0.7232 \|
	\| 0.6507 \| 1.8032 \| 16000 \| 0.7226 \|
	\| 0.653 \| 1.8257 \| 16200 \| 0.7229 \|
	\| 0.6461 \| 1.8483 \| 16400 \| 0.7223 \|
	\| 0.6453 \| 1.8708 \| 16600 \| 0.7221 \|
	\| 0.6534 \| 1.8933 \| 16800 \| 0.7219 \|
	\| 0.6455 \| 1.9159 \| 17000 \| 0.7220 \|
	\| 0.6485 \| 1.9384 \| 17200 \| 0.7212 \|
	\| 0.6536 \| 1.9610 \| 17400 \| 0.7214 \|
	\| 0.6444 \| 1.9835 \| 17600 \| 0.7211 \|
	\| 0.6346 \| 2.0060 \| 17800 \| 0.7356 \|
	\| 0.5929 \| 2.0286 \| 18000 \| 0.7368 \|
	\| 0.5951 \| 2.0511 \| 18200 \| 0.7371 \|
	\| 0.6013 \| 2.0736 \| 18400 \| 0.7374 \|
	\| 0.6004 \| 2.0962 \| 18600 \| 0.7375 \|
	\| 0.5991 \| 2.1187 \| 18800 \| 0.7375 \|
	\| 0.5971 \| 2.1413 \| 19000 \| 0.7369 \|
	\| 0.597 \| 2.1638 \| 19200 \| 0.7380 \|
	\| 0.5951 \| 2.1863 \| 19400 \| 0.7370 \|
	\| 0.5916 \| 2.2089 \| 19600 \| 0.7370 \|
	\| 0.5992 \| 2.2314 \| 19800 \| 0.7372 \|
	\| 0.6011 \| 2.2540 \| 20000 \| 0.7364 \|
	\| 0.6003 \| 2.2765 \| 20200 \| 0.7370 \|
	\| 0.6003 \| 2.2990 \| 20400 \| 0.7370 \|
	\| 0.5985 \| 2.3216 \| 20600 \| 0.7370 \|
	\| 0.5988 \| 2.3441 \| 20800 \| 0.7367 \|
	\| 0.5959 \| 2.3667 \| 21000 \| 0.7370 \|
	\| 0.6019 \| 2.3892 \| 21200 \| 0.7370 \|
	\| 0.5977 \| 2.4117 \| 21400 \| 0.7367 \|
	\| 0.602 \| 2.4343 \| 21600 \| 0.7368 \|
	\| 0.5958 \| 2.4568 \| 21800 \| 0.7368 \|
	\| 0.5969 \| 2.4794 \| 22000 \| 0.7360 \|
	\| 0.6025 \| 2.5019 \| 22200 \| 0.7362 \|
	\| 0.5942 \| 2.5244 \| 22400 \| 0.7361 \|
	\| 0.6006 \| 2.5470 \| 22600 \| 0.7361 \|
	\| 0.5952 \| 2.5695 \| 22800 \| 0.7366 \|
	\| 0.6007 \| 2.5921 \| 23000 \| 0.7363 \|
	\| 0.6003 \| 2.6146 \| 23200 \| 0.7363 \|
	\| 0.6006 \| 2.6371 \| 23400 \| 0.7359 \|
	\| 0.6014 \| 2.6597 \| 23600 \| 0.7360 \|
	\| 0.6008 \| 2.6822 \| 23800 \| 0.7356 \|
	\| 0.6005 \| 2.7048 \| 24000 \| 0.7357 \|
	\| 0.5958 \| 2.7273 \| 24200 \| 0.7356 \|
	\| 0.5977 \| 2.7498 \| 24400 \| 0.7358 \|
	\| 0.6 \| 2.7724 \| 24600 \| 0.7358 \|
	\| 0.5978 \| 2.7949 \| 24800 \| 0.7362 \|
	\| 0.6018 \| 2.8175 \| 25000 \| 0.7359 \|
	\| 0.6079 \| 2.8400 \| 25200 \| 0.7359 \|
	\| 0.6036 \| 2.8625 \| 25400 \| 0.7359 \|
	\| 0.5985 \| 2.8851 \| 25600 \| 0.7359 \|
	\| 0.6019 \| 2.9076 \| 25800 \| 0.7359 \|
	\| 0.5994 \| 2.9302 \| 26000 \| 0.7358 \|
	\| 0.6027 \| 2.9527 \| 26200 \| 0.7358 \|
	\| 0.6014 \| 2.9752 \| 26400 \| 0.7358 \|
	\| 0.5957 \| 2.9978 \| 26600 \| 0.7358 \|


	### Framework versions

	- Transformers 4.43.3
	- Pytorch 2.3.1+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1