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chechdop/
xlm-roberta-sentiment-shp

Name: xlm-roberta-sentiment-shp
Availability: InStock

text-embeddings-inference

endpoints_compatible

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xlm-roberta-sentiment-shp-tokenizer-vocab

This model was trained on the AmericasNLP-2025 dataset for the Task1: Machine Translation in Shipibo-Konibo (shp). It is finetuned on the XLM-Roberta-Base model. It achieves the following results on the evaluation set:

Loss: 1.2232
Accuracy: 0.5823
Precision: 0.5818
Recall: 0.5823
F1: 0.5773
Balanced Accuracy: 0.5507

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: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 500
num_epochs: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Balanced Accuracy
0.8192	1.0	2902	0.8489	0.6340	0.6084	0.6340	0.6083	0.4798
0.8388	2.0	5804	0.7590	0.6857	0.6758	0.6857	0.6604	0.5380
0.6252	3.0	8706	0.8490	0.6930	0.6947	0.6930	0.6756	0.5712
0.7868	4.0	11608	0.9270	0.6930	0.6987	0.6930	0.6836	0.5969
0.5892	5.0	14510	0.8853	0.7019	0.7048	0.7019	0.6998	0.6292
0.719	6.0	17412	1.0641	0.7043	0.6994	0.7043	0.7010	0.6249
0.6805	7.0	20314	1.2283	0.7126	0.7139	0.7126	0.7043	0.6181
0.7471	8.0	23216	1.3237	0.7057	0.6988	0.7057	0.6983	0.6102
0.6011	9.0	26118	1.3071	0.7085	0.6965	0.7085	0.6944	0.5885
0.5917	10.0	29020	1.5666	0.6888	0.6925	0.6888	0.6901	0.6241
0.5057	11.0	31922	1.5296	0.7112	0.7027	0.7112	0.6877	0.5637

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu129
Datasets 4.2.0
Tokenizers 0.22.1

chechdop/xlm-roberta-sentiment-shp

xlm-roberta-sentiment-shp-tokenizer-vocab

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

chechdop/
xlm-roberta-sentiment-shp