scenario-TCR-XLMV_data-en-cardiff_eng_only_beta

haryoaw/scenario-TCR-XLMV_data-en-cardiff_eng_only_beta

scenario-TCR-XLMV_data-en-cardiff_eng_only_beta is a text classification model from haryoaw. 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

3% of all-time downloads

All-time downloads

279

Public

Repo size

6.3 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin3.1 GB · 98%

At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Apr 2, 2024
Updated: Apr 2, 2024
SHA: e047fffb

Try a prompt

Base models

facebook/xlm-v-base

Model card

From the Hugging Face model README

scenario-TCR-XLMV_data-en-cardiff_eng_only_beta

This model is a fine-tuned version of facebook/xlm-v-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.4054
Accuracy: 0.5467
F1: 0.5510

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 112233
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.03	60	1.0887	0.4449	0.3540
No log	2.07	120	1.0211	0.4700	0.3777
No log	3.1	180	1.0598	0.5141	0.4790
No log	4.14	240	1.0131	0.5644	0.5652
No log	5.17	300	1.1073	0.5586	0.5595
No log	6.21	360	1.3697	0.5635	0.5542
No log	7.24	420	1.4910	0.5379	0.5385
No log	8.28	480	1.7325	0.5507	0.5542
0.6649	9.31	540	1.8878	0.5489	0.5505
0.6649	10.34	600	2.2758	0.5309	0.5320
0.6649	11.38	660	2.3053	0.5357	0.5357
0.6649	12.41	720	2.3674	0.5542	0.5574
0.6649	13.45	780	2.7705	0.5309	0.5332
0.6649	14.48	840	2.7515	0.5520	0.5522
0.6649	15.52	900	2.9868	0.5423	0.5447
0.6649	16.55	960	2.7489	0.5582	0.5597
0.1079	17.59	1020	2.8748	0.5525	0.5560
0.1079	18.62	1080	3.0165	0.5467	0.5511
0.1079	19.66	1140	3.2954	0.5340	0.5356
0.1079	20.69	1200	3.1051	0.5441	0.5488
0.1079	21.72	1260	3.2199	0.5441	0.5467
0.1079	22.76	1320	3.1660	0.5454	0.5500
0.1079	23.79	1380	3.2637	0.5445	0.5474
0.1079	24.83	1440	3.2934	0.5538	0.5576
0.0279	25.86	1500	3.2834	0.5476	0.5506
0.0279	26.9	1560	3.3734	0.5467	0.5507
0.0279	27.93	1620	3.4145	0.5437	0.5476
0.0279	28.97	1680	3.4043	0.5454	0.5496
0.0279	30.0	1740	3.4054	0.5467	0.5510

Framework versions

Transformers 4.33.3
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: xlm-roberta
License: mit
Created: Apr 2, 2024
Updated: Apr 2, 2024