ABL_trad_l

mrovejaxd/ABL_trad_l

ABL_trad_l is a text classification model from mrovejaxd. Use it when you need a label for a piece of text. It is set up for transformers.

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

Downloads · 30 days

2% of all-time downloads

All-time downloads

385

Public

Parameters

110M

73.4 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors439 MB · 100%

At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Jun 5, 2024
Updated: Jun 5, 2024
SHA: c2409183

Try a prompt

Base models

dccuchile/bert-base-spanish-wwm-cased

Model card

From the Hugging Face model README

ABL_trad_l

This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.2495
Accuracy: 0.6833
F1: 0.6809

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-06
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 42

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.9228	1.0	2000	0.9053	0.5817	0.5807
0.8293	2.0	4000	0.8649	0.6	0.5927
0.7665	3.0	6000	0.8310	0.6217	0.6206
0.7292	4.0	8000	0.8270	0.6358	0.6316
0.6773	5.0	10000	0.8149	0.6558	0.6520
0.648	6.0	12000	0.8207	0.6492	0.6471
0.5912	7.0	14000	0.8353	0.6508	0.6487
0.5558	8.0	16000	0.8601	0.66	0.6585
0.5169	9.0	18000	0.9048	0.6617	0.6585
0.4678	10.0	20000	0.9497	0.6675	0.6646
0.4281	11.0	22000	1.0488	0.6633	0.6575
0.413	12.0	24000	1.1182	0.66	0.6557
0.389	13.0	26000	1.2184	0.6758	0.6718
0.3501	14.0	28000	1.3527	0.665	0.6613
0.3572	15.0	30000	1.4490	0.6692	0.6642
0.3136	16.0	32000	1.5910	0.6733	0.6713
0.3247	17.0	34000	1.7505	0.6708	0.6683
0.2824	18.0	36000	1.9347	0.6617	0.6551
0.2579	19.0	38000	2.0703	0.6733	0.6692
0.2641	20.0	40000	2.1537	0.6658	0.6609
0.1788	21.0	42000	2.2683	0.6758	0.6728
0.2099	22.0	44000	2.3347	0.6692	0.6670
0.1637	23.0	46000	2.4836	0.675	0.6712
0.1671	24.0	48000	2.5688	0.6775	0.6731
0.1455	25.0	50000	2.6975	0.6767	0.6699
0.1425	26.0	52000	2.7016	0.6742	0.6716
0.1406	27.0	54000	2.7527	0.6825	0.6785
0.1234	28.0	56000	2.8701	0.6758	0.6710
0.0967	29.0	58000	2.8947	0.685	0.6803
0.0864	30.0	60000	2.9296	0.6742	0.6723
0.0956	31.0	62000	2.9966	0.6808	0.6762
0.0835	32.0	64000	3.0406	0.6808	0.6759
0.073	33.0	66000	3.0750	0.6725	0.6680
0.0618	34.0	68000	3.0261	0.6808	0.6769
0.0833	35.0	70000	3.0812	0.685	0.6817
0.0478	36.0	72000	3.1352	0.6825	0.6784
0.0712	37.0	74000	3.1516	0.68	0.6780
0.0712	38.0	76000	3.2088	0.6708	0.6664
0.0407	39.0	78000	3.2520	0.6858	0.6828
0.0659	40.0	80000	3.2791	0.6792	0.6751
0.0468	41.0	82000	3.2433	0.6875	0.6826
0.0571	42.0	84000	3.2495	0.6833	0.6809

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Task: Text Classification
Library: transformers
Type: bert
Created: Jun 5, 2024
Updated: Jun 5, 2024