xlm-roberta-xl-lora

jamesngai/xlm-roberta-xl-lora

xlm-roberta-xl-lora is a machine learning model from jamesngai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

Access

Public

Updated Nov 27, 2023

Repo size

1.4 GB

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Public

Hugging Face

Repo makeup

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At a glance

License: mit
Access: Public
Created: Nov 27, 2023
Updated: Nov 27, 2023
SHA: 70aeb7ab

Base models

facebook/xlm-roberta-xl

Model card

From the Hugging Face model README

xlm-roberta-xl-lora

This model is a fine-tuned version of facebook/xlm-roberta-xl on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.5846
Precision: 0.8927
Recall: 0.9038
F1: 0.8982
Accuracy: 0.9154

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 63
num_epochs: 50
label_smoothing_factor: 0.2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	126	3.4068	0.2417	0.2988	0.2672	0.2522
No log	4.0	252	2.5708	0.5402	0.6641	0.5958	0.6379
No log	6.0	378	2.2050	0.6278	0.7262	0.6734	0.7242
2.8519	8.0	504	2.0050	0.7250	0.7922	0.7571	0.7955
2.8519	10.0	630	1.8831	0.8083	0.8427	0.8252	0.8531
2.8519	12.0	756	1.7923	0.8453	0.8630	0.8540	0.8756
2.8519	14.0	882	1.7371	0.8496	0.8693	0.8593	0.8843
1.8053	16.0	1008	1.7031	0.8529	0.8753	0.8640	0.8886
1.8053	18.0	1134	1.6692	0.8691	0.8812	0.8751	0.8969
1.8053	20.0	1260	1.6555	0.8699	0.8856	0.8777	0.8991
1.8053	22.0	1386	1.6359	0.8824	0.8903	0.8863	0.9054
1.6089	24.0	1512	1.6303	0.8756	0.8919	0.8837	0.9043
1.6089	26.0	1638	1.6169	0.8806	0.8935	0.8870	0.9063
1.6089	28.0	1764	1.6105	0.8876	0.8952	0.8914	0.9088
1.6089	30.0	1890	1.6067	0.8861	0.8981	0.8920	0.9089
1.5373	32.0	2016	1.5998	0.8870	0.8989	0.8929	0.9109
1.5373	34.0	2142	1.5967	0.8900	0.8996	0.8948	0.9121
1.5373	36.0	2268	1.5939	0.8912	0.9015	0.8964	0.9137
1.5373	38.0	2394	1.5922	0.8914	0.9014	0.8964	0.9135
1.501	40.0	2520	1.5894	0.8920	0.9021	0.8970	0.9142
1.501	42.0	2646	1.5874	0.8900	0.9029	0.8964	0.9139
1.501	44.0	2772	1.5865	0.8930	0.9043	0.8986	0.9155
1.501	46.0	2898	1.5866	0.8906	0.9036	0.8971	0.9146
1.4812	48.0	3024	1.5853	0.8907	0.9033	0.8970	0.9148
1.4812	50.0	3150	1.5846	0.8927	0.9038	0.8982	0.9154

Framework versions

Transformers 4.31.0
Pytorch 2.1.0
Datasets 2.14.5
Tokenizers 0.13.3

License: mit
Created: Nov 27, 2023
Updated: Nov 27, 2023