Librarian Bot: Add base_model information to model

e6cb7d6 over 1 year ago

8.04 kB

	---
	license: mit
	tags:
	- generated_from_trainer
	base_model: xlm-roberta-base
	model-index:
	- name: predict-perception-xlmr-focus-object
	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. -->

	# predict-perception-xlmr-focus-object

	This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1927
	- Rmse: 0.5495
	- Rmse Focus::a Su un oggetto: 0.5495
	- Mae: 0.4174
	- Mae Focus::a Su un oggetto: 0.4174
	- R2: 0.5721
	- R2 Focus::a Su un oggetto: 0.5721
	- Cos: 0.5652
	- Pair: 0.0
	- Rank: 0.5
	- Neighbors: 0.5518
	- Rsa: nan

	## 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: 1e-05
	- train_batch_size: 20
	- eval_batch_size: 8
	- seed: 1996
	- 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 \| Rmse \| Rmse Focus::a Su un oggetto \| Mae \| Mae Focus::a Su un oggetto \| R2 \| R2 Focus::a Su un oggetto \| Cos \| Pair \| Rank \| Neighbors \| Rsa \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------------------------:\|:------:\|:--------------------------:\|:-------:\|:-------------------------:\|:-------:\|:----:\|:----:\|:---------:\|:---:\|
	\| 1.0316 \| 1.0 \| 15 \| 0.6428 \| 1.0035 \| 1.0035 \| 0.8806 \| 0.8806 \| -0.4272 \| -0.4272 \| -0.4783 \| 0.0 \| 0.5 \| 0.5302 \| nan \|
	\| 1.0005 \| 2.0 \| 30 \| 0.4564 \| 0.8456 \| 0.8456 \| 0.7078 \| 0.7078 \| -0.0134 \| -0.0134 \| 0.4783 \| 0.0 \| 0.5 \| 0.4440 \| nan \|
	\| 0.9519 \| 3.0 \| 45 \| 0.4151 \| 0.8063 \| 0.8063 \| 0.6797 \| 0.6797 \| 0.0784 \| 0.0784 \| 0.1304 \| 0.0 \| 0.5 \| 0.4888 \| nan \|
	\| 0.92 \| 4.0 \| 60 \| 0.3982 \| 0.7898 \| 0.7898 \| 0.6516 \| 0.6516 \| 0.1159 \| 0.1159 \| 0.2174 \| 0.0 \| 0.5 \| 0.5036 \| nan \|
	\| 0.8454 \| 5.0 \| 75 \| 0.2739 \| 0.6550 \| 0.6550 \| 0.5292 \| 0.5292 \| 0.3919 \| 0.3919 \| 0.6522 \| 0.0 \| 0.5 \| 0.4160 \| nan \|
	\| 0.7247 \| 6.0 \| 90 \| 0.2413 \| 0.6148 \| 0.6148 \| 0.5347 \| 0.5347 \| 0.4642 \| 0.4642 \| 0.4783 \| 0.0 \| 0.5 \| 0.3453 \| nan \|
	\| 0.6055 \| 7.0 \| 105 \| 0.3109 \| 0.6978 \| 0.6978 \| 0.6115 \| 0.6115 \| 0.3098 \| 0.3098 \| 0.4783 \| 0.0 \| 0.5 \| 0.4154 \| nan \|
	\| 0.5411 \| 8.0 \| 120 \| 0.3932 \| 0.7848 \| 0.7848 \| 0.6712 \| 0.6712 \| 0.1271 \| 0.1271 \| 0.4783 \| 0.0 \| 0.5 \| 0.4154 \| nan \|
	\| 0.4784 \| 9.0 \| 135 \| 0.1316 \| 0.4540 \| 0.4540 \| 0.3750 \| 0.3750 \| 0.7079 \| 0.7079 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.4039 \| 10.0 \| 150 \| 0.2219 \| 0.5896 \| 0.5896 \| 0.4954 \| 0.4954 \| 0.5074 \| 0.5074 \| 0.5652 \| 0.0 \| 0.5 \| 0.4838 \| nan \|
	\| 0.3415 \| 11.0 \| 165 \| 0.1935 \| 0.5505 \| 0.5505 \| 0.4443 \| 0.4443 \| 0.5704 \| 0.5704 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.3369 \| 12.0 \| 180 \| 0.2118 \| 0.5761 \| 0.5761 \| 0.4554 \| 0.4554 \| 0.5296 \| 0.5296 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.3083 \| 13.0 \| 195 \| 0.1928 \| 0.5496 \| 0.5496 \| 0.4368 \| 0.4368 \| 0.5718 \| 0.5718 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.2678 \| 14.0 \| 210 \| 0.2205 \| 0.5877 \| 0.5877 \| 0.4472 \| 0.4472 \| 0.5105 \| 0.5105 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.2199 \| 15.0 \| 225 \| 0.2118 \| 0.5760 \| 0.5760 \| 0.4689 \| 0.4689 \| 0.5297 \| 0.5297 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.2238 \| 16.0 \| 240 \| 0.2461 \| 0.6209 \| 0.6209 \| 0.5047 \| 0.5047 \| 0.4537 \| 0.4537 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.2233 \| 17.0 \| 255 \| 0.2307 \| 0.6011 \| 0.6011 \| 0.4618 \| 0.4618 \| 0.4879 \| 0.4879 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.1903 \| 18.0 \| 270 \| 0.2207 \| 0.5880 \| 0.5880 \| 0.4432 \| 0.4432 \| 0.5100 \| 0.5100 \| 0.6522 \| 0.0 \| 0.5 \| 0.6622 \| nan \|
	\| 0.1714 \| 19.0 \| 285 \| 0.2146 \| 0.5798 \| 0.5798 \| 0.4368 \| 0.4368 \| 0.5236 \| 0.5236 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.1759 \| 20.0 \| 300 \| 0.1745 \| 0.5228 \| 0.5228 \| 0.4152 \| 0.4152 \| 0.6126 \| 0.6126 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.1505 \| 21.0 \| 315 \| 0.1944 \| 0.5519 \| 0.5519 \| 0.4170 \| 0.4170 \| 0.5684 \| 0.5684 \| 0.5652 \| 0.0 \| 0.5 \| 0.6247 \| nan \|
	\| 0.1467 \| 22.0 \| 330 \| 0.1802 \| 0.5313 \| 0.5313 \| 0.3910 \| 0.3910 \| 0.5999 \| 0.5999 \| 0.6522 \| 0.0 \| 0.5 \| 0.6622 \| nan \|
	\| 0.1441 \| 23.0 \| 345 \| 0.2360 \| 0.6081 \| 0.6081 \| 0.4755 \| 0.4755 \| 0.4760 \| 0.4760 \| 0.4783 \| 0.0 \| 0.5 \| 0.4938 \| nan \|
	\| 0.1553 \| 24.0 \| 360 \| 0.2129 \| 0.5774 \| 0.5774 \| 0.4539 \| 0.4539 \| 0.5274 \| 0.5274 \| 0.5652 \| 0.0 \| 0.5 \| 0.5518 \| nan \|
	\| 0.1163 \| 25.0 \| 375 \| 0.1780 \| 0.5281 \| 0.5281 \| 0.3952 \| 0.3952 \| 0.6048 \| 0.6048 \| 0.6522 \| 0.0 \| 0.5 \| 0.6622 \| nan \|
	\| 0.1266 \| 26.0 \| 390 \| 0.2163 \| 0.5821 \| 0.5821 \| 0.4569 \| 0.4569 \| 0.5198 \| 0.5198 \| 0.5652 \| 0.0 \| 0.5 \| 0.5518 \| nan \|
	\| 0.1416 \| 27.0 \| 405 \| 0.1829 \| 0.5352 \| 0.5352 \| 0.4082 \| 0.4082 \| 0.5939 \| 0.5939 \| 0.5652 \| 0.0 \| 0.5 \| 0.5518 \| nan \|
	\| 0.1576 \| 28.0 \| 420 \| 0.1930 \| 0.5498 \| 0.5498 \| 0.4126 \| 0.4126 \| 0.5716 \| 0.5716 \| 0.6522 \| 0.0 \| 0.5 \| 0.6622 \| nan \|
	\| 0.118 \| 29.0 \| 435 \| 0.2070 \| 0.5694 \| 0.5694 \| 0.4378 \| 0.4378 \| 0.5405 \| 0.5405 \| 0.5652 \| 0.0 \| 0.5 \| 0.5518 \| nan \|
	\| 0.1179 \| 30.0 \| 450 \| 0.1927 \| 0.5495 \| 0.5495 \| 0.4174 \| 0.4174 \| 0.5721 \| 0.5721 \| 0.5652 \| 0.0 \| 0.5 \| 0.5518 \| nan \|


	### Framework versions

	- Transformers 4.16.2
	- Pytorch 1.10.2+cu113
	- Datasets 1.18.3
	- Tokenizers 0.11.0