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	---
	license: mit
	tags:
	- generated_from_trainer
	model-index:
	- name: gpt-neo-125m-neurallinguisticpioneers
	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. -->

	# gpt-neo-125m-neurallinguisticpioneers

	This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6584

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 5.8245 \| 0.01 \| 1 \| 5.0320 \|
	\| 5.1946 \| 0.01 \| 2 \| 4.2171 \|
	\| 4.1809 \| 0.02 \| 3 \| 3.4349 \|
	\| 3.3553 \| 0.02 \| 4 \| 2.7171 \|
	\| 2.185 \| 0.03 \| 5 \| 2.0634 \|
	\| 1.9955 \| 0.03 \| 6 \| 1.5786 \|
	\| 1.9371 \| 0.04 \| 7 \| 1.2490 \|
	\| 1.4402 \| 0.04 \| 8 \| 1.0349 \|
	\| 0.8763 \| 0.05 \| 9 \| 0.9157 \|
	\| 0.8813 \| 0.05 \| 10 \| 0.8550 \|
	\| 0.7723 \| 0.06 \| 11 \| 0.8259 \|
	\| 0.7909 \| 0.06 \| 12 \| 0.8052 \|
	\| 0.4889 \| 0.07 \| 13 \| 0.7959 \|
	\| 0.7361 \| 0.07 \| 14 \| 0.7891 \|
	\| 0.4922 \| 0.08 \| 15 \| 0.7793 \|
	\| 0.5533 \| 0.09 \| 16 \| 0.7675 \|
	\| 1.1071 \| 0.09 \| 17 \| 0.7563 \|
	\| 0.7885 \| 0.1 \| 18 \| 0.7480 \|
	\| 0.7701 \| 0.1 \| 19 \| 0.7445 \|
	\| 0.6235 \| 0.11 \| 20 \| 0.7447 \|
	\| 0.8623 \| 0.11 \| 21 \| 0.7484 \|
	\| 0.665 \| 0.12 \| 22 \| 0.7558 \|
	\| 0.6907 \| 0.12 \| 23 \| 0.7573 \|
	\| 0.7143 \| 0.13 \| 24 \| 0.7583 \|
	\| 0.7554 \| 0.13 \| 25 \| 0.7599 \|
	\| 0.6228 \| 0.14 \| 26 \| 0.7621 \|
	\| 0.8079 \| 0.14 \| 27 \| 0.7612 \|
	\| 0.6974 \| 0.15 \| 28 \| 0.7586 \|
	\| 0.8349 \| 0.16 \| 29 \| 0.7541 \|
	\| 0.8251 \| 0.16 \| 30 \| 0.7484 \|
	\| 0.687 \| 0.17 \| 31 \| 0.7400 \|
	\| 0.8156 \| 0.17 \| 32 \| 0.7280 \|
	\| 0.7693 \| 0.18 \| 33 \| 0.7183 \|
	\| 0.5224 \| 0.18 \| 34 \| 0.7096 \|
	\| 0.6345 \| 0.19 \| 35 \| 0.7033 \|
	\| 0.6443 \| 0.19 \| 36 \| 0.6979 \|
	\| 1.1552 \| 0.2 \| 37 \| 0.6930 \|
	\| 0.7819 \| 0.2 \| 38 \| 0.6897 \|
	\| 0.6277 \| 0.21 \| 39 \| 0.6875 \|
	\| 0.5751 \| 0.21 \| 40 \| 0.6862 \|
	\| 0.7169 \| 0.22 \| 41 \| 0.6854 \|
	\| 0.7077 \| 0.22 \| 42 \| 0.6842 \|
	\| 0.5667 \| 0.23 \| 43 \| 0.6831 \|
	\| 0.9234 \| 0.24 \| 44 \| 0.6822 \|
	\| 0.6332 \| 0.24 \| 45 \| 0.6815 \|
	\| 0.865 \| 0.25 \| 46 \| 0.6806 \|
	\| 0.5918 \| 0.25 \| 47 \| 0.6797 \|
	\| 0.6196 \| 0.26 \| 48 \| 0.6788 \|
	\| 0.7697 \| 0.26 \| 49 \| 0.6778 \|
	\| 0.4448 \| 0.27 \| 50 \| 0.6769 \|
	\| 0.7951 \| 0.27 \| 51 \| 0.6760 \|
	\| 0.9171 \| 0.28 \| 52 \| 0.6751 \|
	\| 0.7169 \| 0.28 \| 53 \| 0.6745 \|
	\| 0.7001 \| 0.29 \| 54 \| 0.6742 \|
	\| 0.7755 \| 0.29 \| 55 \| 0.6742 \|
	\| 0.7426 \| 0.3 \| 56 \| 0.6743 \|
	\| 0.6208 \| 0.3 \| 57 \| 0.6742 \|
	\| 0.6962 \| 0.31 \| 58 \| 0.6740 \|
	\| 0.3848 \| 0.32 \| 59 \| 0.6739 \|
	\| 0.6986 \| 0.32 \| 60 \| 0.6736 \|
	\| 0.6316 \| 0.33 \| 61 \| 0.6734 \|
	\| 0.5988 \| 0.33 \| 62 \| 0.6732 \|
	\| 0.6551 \| 0.34 \| 63 \| 0.6729 \|
	\| 0.6102 \| 0.34 \| 64 \| 0.6724 \|
	\| 0.7752 \| 0.35 \| 65 \| 0.6718 \|
	\| 0.6145 \| 0.35 \| 66 \| 0.6713 \|
	\| 0.6829 \| 0.36 \| 67 \| 0.6709 \|
	\| 0.7952 \| 0.36 \| 68 \| 0.6705 \|
	\| 0.5888 \| 0.37 \| 69 \| 0.6702 \|
	\| 0.7763 \| 0.37 \| 70 \| 0.6698 \|
	\| 0.6723 \| 0.38 \| 71 \| 0.6694 \|
	\| 0.6429 \| 0.39 \| 72 \| 0.6691 \|
	\| 1.0005 \| 0.39 \| 73 \| 0.6688 \|
	\| 0.6184 \| 0.4 \| 74 \| 0.6684 \|
	\| 0.7118 \| 0.4 \| 75 \| 0.6682 \|
	\| 0.5414 \| 0.41 \| 76 \| 0.6679 \|
	\| 0.6491 \| 0.41 \| 77 \| 0.6676 \|
	\| 0.9418 \| 0.42 \| 78 \| 0.6673 \|
	\| 0.7183 \| 0.42 \| 79 \| 0.6670 \|
	\| 0.682 \| 0.43 \| 80 \| 0.6668 \|
	\| 0.5946 \| 0.43 \| 81 \| 0.6665 \|
	\| 0.6681 \| 0.44 \| 82 \| 0.6662 \|
	\| 0.9125 \| 0.44 \| 83 \| 0.6659 \|
	\| 0.6752 \| 0.45 \| 84 \| 0.6657 \|
	\| 0.6908 \| 0.45 \| 85 \| 0.6655 \|
	\| 0.5878 \| 0.46 \| 86 \| 0.6653 \|
	\| 0.805 \| 0.47 \| 87 \| 0.6651 \|
	\| 0.7584 \| 0.47 \| 88 \| 0.6650 \|
	\| 0.6652 \| 0.48 \| 89 \| 0.6649 \|
	\| 0.9363 \| 0.48 \| 90 \| 0.6647 \|
	\| 0.6201 \| 0.49 \| 91 \| 0.6646 \|
	\| 0.6827 \| 0.49 \| 92 \| 0.6644 \|
	\| 0.8921 \| 0.5 \| 93 \| 0.6643 \|
	\| 0.5194 \| 0.5 \| 94 \| 0.6641 \|
	\| 0.9393 \| 0.51 \| 95 \| 0.6639 \|
	\| 0.8484 \| 0.51 \| 96 \| 0.6637 \|
	\| 0.5412 \| 0.52 \| 97 \| 0.6635 \|
	\| 1.0085 \| 0.52 \| 98 \| 0.6633 \|
	\| 0.5217 \| 0.53 \| 99 \| 0.6632 \|
	\| 0.6137 \| 0.53 \| 100 \| 0.6630 \|
	\| 0.5484 \| 0.54 \| 101 \| 0.6629 \|
	\| 0.5827 \| 0.55 \| 102 \| 0.6627 \|
	\| 0.3374 \| 0.55 \| 103 \| 0.6629 \|
	\| 0.8269 \| 0.56 \| 104 \| 0.6630 \|
	\| 0.8126 \| 0.56 \| 105 \| 0.6630 \|
	\| 0.8088 \| 0.57 \| 106 \| 0.6631 \|
	\| 0.5498 \| 0.57 \| 107 \| 0.6632 \|
	\| 0.6787 \| 0.58 \| 108 \| 0.6633 \|
	\| 0.8786 \| 0.58 \| 109 \| 0.6633 \|
	\| 0.6237 \| 0.59 \| 110 \| 0.6634 \|
	\| 0.6369 \| 0.59 \| 111 \| 0.6634 \|
	\| 0.5629 \| 0.6 \| 112 \| 0.6634 \|
	\| 0.4571 \| 0.6 \| 113 \| 0.6635 \|
	\| 0.902 \| 0.61 \| 114 \| 0.6634 \|
	\| 0.5153 \| 0.61 \| 115 \| 0.6632 \|
	\| 0.9284 \| 0.62 \| 116 \| 0.6629 \|
	\| 0.7149 \| 0.63 \| 117 \| 0.6626 \|
	\| 0.5224 \| 0.63 \| 118 \| 0.6623 \|
	\| 0.5969 \| 0.64 \| 119 \| 0.6621 \|
	\| 0.655 \| 0.64 \| 120 \| 0.6619 \|
	\| 0.6182 \| 0.65 \| 121 \| 0.6619 \|
	\| 0.6564 \| 0.65 \| 122 \| 0.6618 \|
	\| 0.6919 \| 0.66 \| 123 \| 0.6618 \|
	\| 0.5894 \| 0.66 \| 124 \| 0.6617 \|
	\| 0.4312 \| 0.67 \| 125 \| 0.6617 \|
	\| 0.7523 \| 0.67 \| 126 \| 0.6617 \|
	\| 0.7962 \| 0.68 \| 127 \| 0.6617 \|
	\| 0.3758 \| 0.68 \| 128 \| 0.6617 \|
	\| 0.7343 \| 0.69 \| 129 \| 0.6617 \|
	\| 0.7569 \| 0.7 \| 130 \| 0.6616 \|
	\| 0.4816 \| 0.7 \| 131 \| 0.6616 \|
	\| 0.7127 \| 0.71 \| 132 \| 0.6616 \|
	\| 0.4597 \| 0.71 \| 133 \| 0.6616 \|
	\| 0.6429 \| 0.72 \| 134 \| 0.6616 \|
	\| 0.6452 \| 0.72 \| 135 \| 0.6616 \|
	\| 0.5815 \| 0.73 \| 136 \| 0.6615 \|
	\| 0.743 \| 0.73 \| 137 \| 0.6614 \|
	\| 0.5613 \| 0.74 \| 138 \| 0.6612 \|
	\| 0.5038 \| 0.74 \| 139 \| 0.6610 \|
	\| 0.797 \| 0.75 \| 140 \| 0.6609 \|
	\| 0.6244 \| 0.75 \| 141 \| 0.6608 \|
	\| 0.4257 \| 0.76 \| 142 \| 0.6607 \|
	\| 0.6096 \| 0.76 \| 143 \| 0.6606 \|
	\| 0.6566 \| 0.77 \| 144 \| 0.6605 \|
	\| 0.4325 \| 0.78 \| 145 \| 0.6604 \|
	\| 0.7307 \| 0.78 \| 146 \| 0.6604 \|
	\| 0.7955 \| 0.79 \| 147 \| 0.6603 \|
	\| 0.6972 \| 0.79 \| 148 \| 0.6602 \|
	\| 0.7527 \| 0.8 \| 149 \| 0.6602 \|
	\| 0.5718 \| 0.8 \| 150 \| 0.6602 \|
	\| 0.8002 \| 0.81 \| 151 \| 0.6602 \|
	\| 0.6643 \| 0.81 \| 152 \| 0.6602 \|
	\| 0.7817 \| 0.82 \| 153 \| 0.6602 \|
	\| 0.6829 \| 0.82 \| 154 \| 0.6602 \|
	\| 0.8392 \| 0.83 \| 155 \| 0.6601 \|
	\| 0.5246 \| 0.83 \| 156 \| 0.6601 \|
	\| 0.6613 \| 0.84 \| 157 \| 0.6601 \|
	\| 0.4456 \| 0.84 \| 158 \| 0.6600 \|
	\| 0.4505 \| 0.85 \| 159 \| 0.6600 \|
	\| 0.6184 \| 0.86 \| 160 \| 0.6600 \|
	\| 0.6419 \| 0.86 \| 161 \| 0.6599 \|
	\| 0.3138 \| 0.87 \| 162 \| 0.6599 \|
	\| 0.5554 \| 0.87 \| 163 \| 0.6598 \|
	\| 0.702 \| 0.88 \| 164 \| 0.6597 \|
	\| 0.801 \| 0.88 \| 165 \| 0.6595 \|
	\| 0.6689 \| 0.89 \| 166 \| 0.6594 \|
	\| 0.5907 \| 0.89 \| 167 \| 0.6593 \|
	\| 0.9349 \| 0.9 \| 168 \| 0.6592 \|
	\| 0.7987 \| 0.9 \| 169 \| 0.6591 \|
	\| 0.6379 \| 0.91 \| 170 \| 0.6590 \|
	\| 0.5561 \| 0.91 \| 171 \| 0.6589 \|
	\| 0.6637 \| 0.92 \| 172 \| 0.6589 \|
	\| 0.5391 \| 0.93 \| 173 \| 0.6588 \|
	\| 0.6578 \| 0.93 \| 174 \| 0.6588 \|
	\| 0.7013 \| 0.94 \| 175 \| 0.6587 \|
	\| 0.6868 \| 0.94 \| 176 \| 0.6587 \|
	\| 0.6297 \| 0.95 \| 177 \| 0.6586 \|
	\| 0.7349 \| 0.95 \| 178 \| 0.6586 \|
	\| 0.8577 \| 0.96 \| 179 \| 0.6585 \|
	\| 0.8536 \| 0.96 \| 180 \| 0.6585 \|
	\| 0.4971 \| 0.97 \| 181 \| 0.6585 \|
	\| 0.5129 \| 0.97 \| 182 \| 0.6585 \|
	\| 0.7636 \| 0.98 \| 183 \| 0.6585 \|
	\| 0.5111 \| 0.98 \| 184 \| 0.6585 \|
	\| 0.7281 \| 0.99 \| 185 \| 0.6585 \|
	\| 0.5653 \| 0.99 \| 186 \| 0.6585 \|
	\| 0.7766 \| 1.0 \| 187 \| 0.6584 \|


	### Framework versions

	- Transformers 4.28.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.12.0
	- Tokenizers 0.13.3