FLANG-BERT_roberta-base – AI Model by avinasht | AlphaNeural AI

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avinasht/
FLANG-BERT_roberta-base

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text-classification

generated_from_trainer

SALT-NLP/FLANG-BERT

autotrain_compatible

endpoints_compatible

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FLANG-BERT_roberta-base

This model is a fine-tuned version of SALT-NLP/FLANG-BERT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5101
Accuracy: 0.8643
F1: 0.8637
Precision: 0.8638
Recall: 0.8643

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: 0.0001
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
lr_scheduler_warmup_steps: 1000
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.841	1.0	91	0.7542	0.6895	0.6505	0.7281	0.6895
0.4766	2.0	182	0.4469	0.8159	0.8161	0.8201	0.8159
0.3539	3.0	273	0.3916	0.8456	0.8459	0.8473	0.8456
0.2452	4.0	364	0.4667	0.8362	0.8348	0.8369	0.8362
0.1646	5.0	455	0.4408	0.8643	0.8636	0.8643	0.8643
0.1273	6.0	546	0.5101	0.8643	0.8637	0.8638	0.8643
0.1052	7.0	637	0.7249	0.8393	0.8369	0.8413	0.8393
0.0889	8.0	728	0.5791	0.8424	0.8413	0.8419	0.8424
0.0846	9.0	819	0.5522	0.8580	0.8576	0.8577	0.8580
0.0764	10.0	910	0.7277	0.8549	0.8549	0.8555	0.8549
0.1531	11.0	1001	0.6068	0.8424	0.8407	0.8441	0.8424

Framework versions

Transformers 4.37.0
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.1