bbau-semeval25_fold4

g-assismoraes/bbau-semeval25_fold4

bbau-semeval25_fold4 is a text classification model from g-assismoraes. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

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

Downloads · 30 days

13% of all-time downloads

All-time downloads

Public

Parameters

109M

872 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors436 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Oct 28, 2024
Updated: Oct 28, 2024
SHA: c31c2a2b

Try a prompt

Base models

neuralmind/bert-base-portuguese-cased

Model card

From the Hugging Face model README

bbau-semeval25_fold4

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4458
Precision Samples: 1.0
Recall Samples: 0.0
F1 Samples: 0.0
Precision Macro: 1.0
Recall Macro: 0.4091
F1 Macro: 0.4091
Precision Micro: 1.0
Recall Micro: 0.0
F1 Micro: 0.0
Precision Weighted: 1.0
Recall Weighted: 0.0
F1 Weighted: 0.0

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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Samples	Recall Samples	F1 Samples	Precision Macro	Recall Macro	F1 Macro	Precision Micro	Recall Micro	F1 Micro	Precision Weighted	Recall Weighted	F1 Weighted
No log	1.0	5	0.6306	0.0699	0.2911	0.1095	0.5166	0.6006	0.2907	0.0704	0.3585	0.1176	0.4119	0.3585	0.1190
0.6408	2.0	10	0.5798	0.0481	0.1398	0.0666	0.7414	0.4794	0.3825	0.0505	0.1321	0.0731	0.5897	0.1321	0.0513
0.6408	3.0	15	0.5419	0.05	0.0175	0.0243	0.8951	0.4394	0.3810	0.0326	0.0283	0.0303	0.8880	0.0283	0.0023
0.5512	4.0	20	0.5123	0.0625	0.0092	0.0143	0.9705	0.4242	0.4106	0.0392	0.0189	0.0255	0.9727	0.0189	0.0018
0.5512	5.0	25	0.4900	0.975	0.005	0.0083	0.9924	0.4167	0.4167	0.5	0.0094	0.0185	0.9906	0.0094	0.0094
0.5002	6.0	30	0.4730	1.0	0.0	0.0	1.0	0.4091	0.4091	1.0	0.0	0.0	1.0	0.0	0.0
0.5002	7.0	35	0.4612	1.0	0.0	0.0	1.0	0.4091	0.4091	1.0	0.0	0.0	1.0	0.0	0.0
0.4705	8.0	40	0.4529	1.0	0.0	0.0	1.0	0.4091	0.4091	1.0	0.0	0.0	1.0	0.0	0.0
0.4705	9.0	45	0.4477	1.0	0.0	0.0	1.0	0.4091	0.4091	1.0	0.0	0.0	1.0	0.0	0.0
0.4562	10.0	50	0.4458	1.0	0.0	0.0	1.0	0.4091	0.4091	1.0	0.0	0.0	1.0	0.0	0.0

Framework versions

Transformers 4.46.0
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.20.1

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Oct 28, 2024
Updated: Oct 28, 2024