ES-ENG-xlm-roberta-sentiment

MinaAlmasi/ES-ENG-xlm-roberta-sentiment

ES-ENG-xlm-roberta-sentiment is a text classification model from MinaAlmasi. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.

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

Downloads · 30 days

21% of all-time downloads

All-time downloads

222

Public

Repo size

118 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin1.1 GB · 98%

At a glance

Task: Text Classification
Library: transformers
License: cc-by-nc-4.0
Model type: xlm-roberta
Access: Public
Created: May 19, 2023
Updated: May 22, 2023
SHA: 6d6a8fee

Try a prompt

Model card

From the Hugging Face model README

ES-ENG-xlm-roberta-sentiment

This model is a fine-tuned version of xlm-roberta-base on a Custom dataset.

The best model (stopped after 20 epochs) achieves the following results on the evaluation set:

Loss: 0.7743
Accuracy: 0.6702
F1: 0.6672
Precision: 0.6664
Recall: 0.6702

Intended uses & limitations

Note that commercial use with this model is prohibited.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-06
train_batch_size: 64
eval_batch_size: 64
seed: 42
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	Accuracy	F1	Precision	Recall
1.1099	1.0	208	1.0718	0.3968	0.3851	0.4857	0.3968
1.0057	2.0	416	0.8926	0.5492	0.5080	0.5639	0.5492
0.8988	3.0	624	0.8384	0.5883	0.5792	0.5789	0.5883
0.8606	4.0	832	0.8209	0.6168	0.6086	0.6086	0.6168
0.8338	5.0	1040	0.8006	0.6120	0.6068	0.6046	0.6120
0.8081	6.0	1248	0.8074	0.6026	0.5935	0.5966	0.6026
0.7872	7.0	1456	0.7786	0.6194	0.6149	0.6127	0.6194
0.7624	8.0	1664	0.7783	0.6379	0.6277	0.6342	0.6379
0.7446	9.0	1872	0.7643	0.6366	0.6287	0.6314	0.6366
0.7274	10.0	2080	0.7846	0.6395	0.6297	0.6351	0.6395
0.7116	11.0	2288	0.7465	0.6495	0.6425	0.6462	0.6495
0.6998	12.0	2496	0.7599	0.6537	0.6474	0.6494	0.6537
0.6852	13.0	2704	0.7651	0.6515	0.6443	0.6465	0.6515
0.6726	14.0	2912	0.7571	0.6576	0.6536	0.6530	0.6576
0.6665	15.0	3120	0.7597	0.6557	0.6506	0.6514	0.6557
0.6541	16.0	3328	0.7590	0.6615	0.6584	0.6576	0.6615
0.6513	17.0	3536	0.7617	0.6599	0.6544	0.6555	0.6599
0.6392	18.0	3744	0.7740	0.6628	0.6585	0.6582	0.6628
0.6369	19.0	3952	0.7666	0.6631	0.6588	0.6585	0.6631
0.6268	20.0	4160	0.7743	0.6702	0.6672	0.6664	0.6702
0.62	21.0	4368	0.7712	0.6680	0.6638	0.6638	0.6680
0.619	22.0	4576	0.7720	0.6689	0.6656	0.6649	0.6689
0.6074	23.0	4784	0.7729	0.6663	0.6630	0.6621	0.6663

Framework versions

Transformers 4.29.1
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: xlm-roberta
License: cc-by-nc-4.0
Created: May 19, 2023
Updated: May 22, 2023