emotions_flan_tf / README.md

yunase/flant5_QLORA_model_emotion_detection

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	---
	license: apache-2.0
	library_name: peft
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	base_model: google/flan-t5-base
	model-index:
	- name: emotions_flan_tf
	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. -->

	# emotions_flan_tf

	This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co./google/flan-t5-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4970
	- F1 Micro: 0.6980
	- F1 Macro: 0.6126
	- Accuracy: 0.2188

	## 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: 16
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Micro \| F1 Macro \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:--------:\|
	\| 0.8313 \| 0.21 \| 20 \| 0.7916 \| 0.4215 \| 0.1586 \| 0.0123 \|
	\| 0.7813 \| 0.41 \| 40 \| 0.7840 \| 0.4717 \| 0.2286 \| 0.0201 \|
	\| 0.7702 \| 0.62 \| 60 \| 0.7485 \| 0.4935 \| 0.2379 \| 0.0971 \|
	\| 0.7218 \| 0.83 \| 80 \| 0.6327 \| 0.6045 \| 0.3866 \| 0.1256 \|
	\| 0.6401 \| 1.03 \| 100 \| 0.5907 \| 0.6269 \| 0.4310 \| 0.1586 \|
	\| 0.5951 \| 1.24 \| 120 \| 0.5668 \| 0.6459 \| 0.4981 \| 0.1502 \|
	\| 0.5686 \| 1.45 \| 140 \| 0.5458 \| 0.6593 \| 0.5372 \| 0.1683 \|
	\| 0.5576 \| 1.65 \| 160 \| 0.5332 \| 0.6675 \| 0.5403 \| 0.1722 \|
	\| 0.5465 \| 1.86 \| 180 \| 0.5224 \| 0.6734 \| 0.5667 \| 0.1812 \|
	\| 0.5436 \| 2.07 \| 200 \| 0.5164 \| 0.6807 \| 0.5751 \| 0.1877 \|
	\| 0.5297 \| 2.27 \| 220 \| 0.5149 \| 0.6742 \| 0.5793 \| 0.1741 \|
	\| 0.5109 \| 2.48 \| 240 \| 0.5049 \| 0.6845 \| 0.5824 \| 0.1929 \|
	\| 0.5265 \| 2.69 \| 260 \| 0.5070 \| 0.6846 \| 0.5859 \| 0.1799 \|
	\| 0.5028 \| 2.89 \| 280 \| 0.5068 \| 0.6847 \| 0.5870 \| 0.1864 \|
	\| 0.5097 \| 3.1 \| 300 \| 0.5025 \| 0.6892 \| 0.5940 \| 0.2084 \|
	\| 0.4971 \| 3.31 \| 320 \| 0.5032 \| 0.6843 \| 0.5995 \| 0.1890 \|
	\| 0.4762 \| 3.51 \| 340 \| 0.5069 \| 0.6955 \| 0.5928 \| 0.2129 \|
	\| 0.4811 \| 3.72 \| 360 \| 0.4954 \| 0.6898 \| 0.5996 \| 0.2026 \|
	\| 0.5065 \| 3.93 \| 380 \| 0.4961 \| 0.6918 \| 0.6038 \| 0.1838 \|
	\| 0.4746 \| 4.13 \| 400 \| 0.4992 \| 0.6956 \| 0.6009 \| 0.2142 \|
	\| 0.4786 \| 4.34 \| 420 \| 0.5013 \| 0.6918 \| 0.6018 \| 0.2026 \|
	\| 0.4832 \| 4.55 \| 440 \| 0.4935 \| 0.6904 \| 0.6031 \| 0.2155 \|
	\| 0.465 \| 4.75 \| 460 \| 0.4984 \| 0.6938 \| 0.6027 \| 0.2071 \|
	\| 0.4683 \| 4.96 \| 480 \| 0.4977 \| 0.6960 \| 0.6011 \| 0.2091 \|
	\| 0.4573 \| 5.17 \| 500 \| 0.4985 \| 0.6915 \| 0.6076 \| 0.2006 \|
	\| 0.4619 \| 5.37 \| 520 \| 0.4952 \| 0.6945 \| 0.6044 \| 0.2129 \|
	\| 0.4535 \| 5.58 \| 540 \| 0.4983 \| 0.6927 \| 0.6024 \| 0.2078 \|
	\| 0.4475 \| 5.79 \| 560 \| 0.4967 \| 0.6970 \| 0.6064 \| 0.2194 \|
	\| 0.454 \| 5.99 \| 580 \| 0.5027 \| 0.6941 \| 0.6090 \| 0.1994 \|
	\| 0.4479 \| 6.2 \| 600 \| 0.4940 \| 0.6919 \| 0.6041 \| 0.2117 \|
	\| 0.4304 \| 6.41 \| 620 \| 0.5002 \| 0.6982 \| 0.6114 \| 0.2006 \|
	\| 0.445 \| 6.61 \| 640 \| 0.4970 \| 0.6951 \| 0.6098 \| 0.2071 \|
	\| 0.4434 \| 6.82 \| 660 \| 0.4964 \| 0.6976 \| 0.6075 \| 0.2136 \|
	\| 0.4543 \| 7.03 \| 680 \| 0.4904 \| 0.6936 \| 0.6086 \| 0.2013 \|
	\| 0.4474 \| 7.24 \| 700 \| 0.4969 \| 0.6960 \| 0.6108 \| 0.2071 \|
	\| 0.4325 \| 7.44 \| 720 \| 0.4998 \| 0.7013 \| 0.6123 \| 0.2123 \|
	\| 0.4362 \| 7.65 \| 740 \| 0.4947 \| 0.6953 \| 0.6101 \| 0.2091 \|
	\| 0.4276 \| 7.86 \| 760 \| 0.4978 \| 0.6955 \| 0.6119 \| 0.2052 \|
	\| 0.4392 \| 8.06 \| 780 \| 0.4944 \| 0.6967 \| 0.6078 \| 0.2104 \|
	\| 0.4167 \| 8.27 \| 800 \| 0.4987 \| 0.6966 \| 0.6080 \| 0.2097 \|
	\| 0.4309 \| 8.48 \| 820 \| 0.4970 \| 0.6980 \| 0.6126 \| 0.2188 \|
	\| 0.42 \| 8.68 \| 840 \| 0.4999 \| 0.6977 \| 0.6105 \| 0.2129 \|
	\| 0.423 \| 8.89 \| 860 \| 0.5003 \| 0.6975 \| 0.6087 \| 0.2142 \|
	\| 0.4382 \| 9.1 \| 880 \| 0.4977 \| 0.6975 \| 0.6115 \| 0.2136 \|
	\| 0.4182 \| 9.3 \| 900 \| 0.4976 \| 0.6981 \| 0.6123 \| 0.2155 \|
	\| 0.4153 \| 9.51 \| 920 \| 0.5000 \| 0.6978 \| 0.6108 \| 0.2175 \|
	\| 0.4277 \| 9.72 \| 940 \| 0.5003 \| 0.6982 \| 0.6092 \| 0.2168 \|
	\| 0.4246 \| 9.92 \| 960 \| 0.5000 \| 0.6976 \| 0.6093 \| 0.2168 \|


	### Framework versions

	- PEFT 0.10.0
	- Transformers 4.39.3
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2

	---
	license: apache-2.0
	library_name: peft
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	base_model: google/flan-t5-base
	model-index:
	- name: emotions_flan_tf
	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. -->

	# emotions_flan_tf

	This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co./google/flan-t5-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4970
	- F1 Micro: 0.6980
	- F1 Macro: 0.6126
	- Accuracy: 0.2188

	## 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: 16
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Micro \| F1 Macro \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:--------:\|
	\| 0.8313 \| 0.21 \| 20 \| 0.7916 \| 0.4215 \| 0.1586 \| 0.0123 \|
	\| 0.7813 \| 0.41 \| 40 \| 0.7840 \| 0.4717 \| 0.2286 \| 0.0201 \|
	\| 0.7702 \| 0.62 \| 60 \| 0.7485 \| 0.4935 \| 0.2379 \| 0.0971 \|
	\| 0.7218 \| 0.83 \| 80 \| 0.6327 \| 0.6045 \| 0.3866 \| 0.1256 \|
	\| 0.6401 \| 1.03 \| 100 \| 0.5907 \| 0.6269 \| 0.4310 \| 0.1586 \|
	\| 0.5951 \| 1.24 \| 120 \| 0.5668 \| 0.6459 \| 0.4981 \| 0.1502 \|
	\| 0.5686 \| 1.45 \| 140 \| 0.5458 \| 0.6593 \| 0.5372 \| 0.1683 \|
	\| 0.5576 \| 1.65 \| 160 \| 0.5332 \| 0.6675 \| 0.5403 \| 0.1722 \|
	\| 0.5465 \| 1.86 \| 180 \| 0.5224 \| 0.6734 \| 0.5667 \| 0.1812 \|
	\| 0.5436 \| 2.07 \| 200 \| 0.5164 \| 0.6807 \| 0.5751 \| 0.1877 \|
	\| 0.5297 \| 2.27 \| 220 \| 0.5149 \| 0.6742 \| 0.5793 \| 0.1741 \|
	\| 0.5109 \| 2.48 \| 240 \| 0.5049 \| 0.6845 \| 0.5824 \| 0.1929 \|
	\| 0.5265 \| 2.69 \| 260 \| 0.5070 \| 0.6846 \| 0.5859 \| 0.1799 \|
	\| 0.5028 \| 2.89 \| 280 \| 0.5068 \| 0.6847 \| 0.5870 \| 0.1864 \|
	\| 0.5097 \| 3.1 \| 300 \| 0.5025 \| 0.6892 \| 0.5940 \| 0.2084 \|
	\| 0.4971 \| 3.31 \| 320 \| 0.5032 \| 0.6843 \| 0.5995 \| 0.1890 \|
	\| 0.4762 \| 3.51 \| 340 \| 0.5069 \| 0.6955 \| 0.5928 \| 0.2129 \|
	\| 0.4811 \| 3.72 \| 360 \| 0.4954 \| 0.6898 \| 0.5996 \| 0.2026 \|
	\| 0.5065 \| 3.93 \| 380 \| 0.4961 \| 0.6918 \| 0.6038 \| 0.1838 \|
	\| 0.4746 \| 4.13 \| 400 \| 0.4992 \| 0.6956 \| 0.6009 \| 0.2142 \|
	\| 0.4786 \| 4.34 \| 420 \| 0.5013 \| 0.6918 \| 0.6018 \| 0.2026 \|
	\| 0.4832 \| 4.55 \| 440 \| 0.4935 \| 0.6904 \| 0.6031 \| 0.2155 \|
	\| 0.465 \| 4.75 \| 460 \| 0.4984 \| 0.6938 \| 0.6027 \| 0.2071 \|
	\| 0.4683 \| 4.96 \| 480 \| 0.4977 \| 0.6960 \| 0.6011 \| 0.2091 \|
	\| 0.4573 \| 5.17 \| 500 \| 0.4985 \| 0.6915 \| 0.6076 \| 0.2006 \|
	\| 0.4619 \| 5.37 \| 520 \| 0.4952 \| 0.6945 \| 0.6044 \| 0.2129 \|
	\| 0.4535 \| 5.58 \| 540 \| 0.4983 \| 0.6927 \| 0.6024 \| 0.2078 \|
	\| 0.4475 \| 5.79 \| 560 \| 0.4967 \| 0.6970 \| 0.6064 \| 0.2194 \|
	\| 0.454 \| 5.99 \| 580 \| 0.5027 \| 0.6941 \| 0.6090 \| 0.1994 \|
	\| 0.4479 \| 6.2 \| 600 \| 0.4940 \| 0.6919 \| 0.6041 \| 0.2117 \|
	\| 0.4304 \| 6.41 \| 620 \| 0.5002 \| 0.6982 \| 0.6114 \| 0.2006 \|
	\| 0.445 \| 6.61 \| 640 \| 0.4970 \| 0.6951 \| 0.6098 \| 0.2071 \|
	\| 0.4434 \| 6.82 \| 660 \| 0.4964 \| 0.6976 \| 0.6075 \| 0.2136 \|
	\| 0.4543 \| 7.03 \| 680 \| 0.4904 \| 0.6936 \| 0.6086 \| 0.2013 \|
	\| 0.4474 \| 7.24 \| 700 \| 0.4969 \| 0.6960 \| 0.6108 \| 0.2071 \|
	\| 0.4325 \| 7.44 \| 720 \| 0.4998 \| 0.7013 \| 0.6123 \| 0.2123 \|
	\| 0.4362 \| 7.65 \| 740 \| 0.4947 \| 0.6953 \| 0.6101 \| 0.2091 \|
	\| 0.4276 \| 7.86 \| 760 \| 0.4978 \| 0.6955 \| 0.6119 \| 0.2052 \|
	\| 0.4392 \| 8.06 \| 780 \| 0.4944 \| 0.6967 \| 0.6078 \| 0.2104 \|
	\| 0.4167 \| 8.27 \| 800 \| 0.4987 \| 0.6966 \| 0.6080 \| 0.2097 \|
	\| 0.4309 \| 8.48 \| 820 \| 0.4970 \| 0.6980 \| 0.6126 \| 0.2188 \|
	\| 0.42 \| 8.68 \| 840 \| 0.4999 \| 0.6977 \| 0.6105 \| 0.2129 \|
	\| 0.423 \| 8.89 \| 860 \| 0.5003 \| 0.6975 \| 0.6087 \| 0.2142 \|
	\| 0.4382 \| 9.1 \| 880 \| 0.4977 \| 0.6975 \| 0.6115 \| 0.2136 \|
	\| 0.4182 \| 9.3 \| 900 \| 0.4976 \| 0.6981 \| 0.6123 \| 0.2155 \|
	\| 0.4153 \| 9.51 \| 920 \| 0.5000 \| 0.6978 \| 0.6108 \| 0.2175 \|
	\| 0.4277 \| 9.72 \| 940 \| 0.5003 \| 0.6982 \| 0.6092 \| 0.2168 \|
	\| 0.4246 \| 9.92 \| 960 \| 0.5000 \| 0.6976 \| 0.6093 \| 0.2168 \|


	### Framework versions

	- PEFT 0.10.0
	- Transformers 4.39.3
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2