fewshot-CDW-CE-250-samples

Davidozito/fewshot-CDW-CE-250-samples

fewshot-CDW-CE-250-samples is a text classification model from Davidozito. 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

45% of all-time downloads

All-time downloads

Public

Parameters

278M

1.1 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors1.1 GB · 98%

At a glance

Task: Text Classification
Library: transformers
Model type: xlm-roberta
Access: Public
Created: Jun 21, 2025
Updated: Jun 21, 2025
SHA: a713cab2

Try a prompt

Base models

Davidozito/zeroshot-classification

Model card

From the Hugging Face model README

fewshot-CDW-CE-250-samples

This model is a fine-tuned version of Davidozito/zeroshot-classification on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.1342
Accuracy: 0.52
F1 Macro: 0.4567
F1 Weighted: 0.4859
Precision Macro: 0.4891
Recall Macro: 0.4833

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: 8
eval_batch_size: 8
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Weighted	Precision Macro	Recall Macro
1.1607	0.2414	7	1.1334	0.48	0.4406	0.4664	0.48	0.45
1.198	0.4828	14	1.1342	0.52	0.4567	0.4859	0.4891	0.4833
1.0988	0.7241	21	1.1320	0.48	0.4405	0.4777	0.4678	0.4400
1.0673	0.9655	28	1.1304	0.48	0.4405	0.4777	0.4678	0.4400
1.2669	1.2069	35	1.1282	0.48	0.4405	0.4777	0.4678	0.4400
1.0031	1.4483	42	1.1274	0.48	0.4405	0.4777	0.4678	0.4400
1.264	1.6897	49	1.1288	0.48	0.4405	0.4777	0.4678	0.4400
1.0977	1.9310	56	1.1292	0.48	0.4405	0.4777	0.4678	0.4400
1.2181	2.1724	63	1.1300	0.48	0.4405	0.4777	0.4678	0.4400
1.1506	2.4138	70	1.1310	0.48	0.4405	0.4777	0.4678	0.4400
1.0379	2.6552	77	1.1300	0.48	0.4405	0.4777	0.4678	0.4400
1.1612	2.8966	84	1.1277	0.48	0.4405	0.4777	0.4678	0.4400
1.1059	3.1379	91	1.1273	0.48	0.4405	0.4777	0.4678	0.4400
1.1636	3.3793	98	1.1267	0.48	0.4405	0.4777	0.4678	0.4400
1.0985	3.6207	105	1.1266	0.48	0.4405	0.4777	0.4678	0.4400
1.0929	3.8621	112	1.1267	0.48	0.4405	0.4777	0.4678	0.4400
0.9983	4.1034	119	1.1266	0.48	0.4405	0.4777	0.4678	0.4400
1.1553	4.3448	126	1.1266	0.48	0.4405	0.4777	0.4678	0.4400
1.2561	4.5862	133	1.1270	0.48	0.4405	0.4777	0.4678	0.4400
1.0976	4.8276	140	1.1271	0.48	0.4405	0.4777	0.4678	0.4400

Framework versions

Transformers 4.52.4
Pytorch 2.7.1
Datasets 3.6.0
Tokenizers 0.21.1

Task: Text Classification
Library: transformers
Type: xlm-roberta
Created: Jun 21, 2025
Updated: Jun 21, 2025