M2SA-text-only

FFZG-cleopatra/M2SA-text-only

M2SA-text-only is a text classification model from FFZG-cleopatra. 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. --

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At a glance

Task: Text Classification
Library: transformers
Model type: xlm-roberta
Access: Public
Created: May 16, 2024
Updated: Jun 5, 2024
SHA: 9291c54d

Try a prompt

Base models

cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual

Datasets

Spaces

FFZG-cleopatra/M2SA-demo-text-only

Model card

From the Hugging Face model README

cardiffnlp-twitter-xlmr-finetuned-txtnly-all-42

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual on the all dataset. It achieves the following results on the evaluation set:

Loss: 0.6972
Precision: 0.6687
Recall: 0.6729
F1: 0.6703

Model description

More information needed

Usage

To use the model use the following script. Kindly set the device based on availability of the GPU.

from transformers import (pipeline)

analyzer = pipeline(
    "sentiment-analysis", model="FFZG-cleopatra/M2SA-text-only"
)

input_text = "I feel amazing today."
print(analyzer(input_text)[0]["label"])

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.6122	0.06	500	0.8542	0.6559	0.4905	0.4841
0.5497	0.12	1000	0.8037	0.7044	0.6070	0.6209
0.5404	0.18	1500	0.9700	0.5591	0.4176	0.3652
0.5165	0.24	2000	0.7449	0.7349	0.5297	0.5369
0.5136	0.3	2500	0.7885	0.6766	0.5025	0.5001
0.5072	0.36	3000	0.8124	0.6076	0.6132	0.5917
0.5011	0.42	3500	0.8767	0.6427	0.5987	0.5784
0.5021	0.48	4000	0.7958	0.6848	0.6362	0.6503
0.4946	0.54	4500	0.8045	0.7220	0.4968	0.4983
0.4928	0.6	5000	0.7803	0.7582	0.5381	0.5503
0.5008	0.66	5500	0.7507	0.4407	0.4798	0.4594
0.4966	0.72	6000	0.8239	0.6140	0.6767	0.6311
0.4791	0.78	6500	0.7028	0.6568	0.5206	0.5413
0.494	0.84	7000	0.8034	0.6660	0.5189	0.5227
0.4861	0.9	7500	0.9003	0.5781	0.4785	0.4541
0.4804	0.96	8000	0.7740	0.6239	0.5775	0.5792
0.4614	1.02	8500	0.7397	0.6848	0.6312	0.6471
0.4315	1.08	9000	0.7889	0.6642	0.6035	0.6149
0.4506	1.14	9500	0.8784	0.6387	0.5017	0.4968
0.4489	1.2	10000	0.7994	0.5340	0.4964	0.4949
0.4466	1.26	10500	0.8110	0.5776	0.4735	0.4464
0.4319	1.32	11000	0.8069	0.6612	0.5399	0.5481
0.4243	1.38	11500	0.7942	0.5948	0.5705	0.5797
0.4398	1.44	12000	0.9738	0.5370	0.6070	0.5247
0.4526	1.5	12500	0.7196	0.7046	0.5478	0.5590
0.4529	1.56	13000	0.8050	0.6419	0.5731	0.5863
0.446	1.62	13500	0.7564	0.6521	0.5912	0.6107
0.4315	1.68	14000	0.7515	0.6475	0.6069	0.6212
0.4464	1.74	14500	0.8308	0.6276	0.5513	0.5599
0.4423	1.8	15000	0.7982	0.6176	0.5937	0.5992
0.4551	1.86	15500	0.8223	0.6356	0.5934	0.6020
0.4408	1.92	16000	0.7691	0.6088	0.5147	0.5131
0.4389	1.98	16500	0.6972	0.6687	0.6729	0.6703
0.3886	2.04	17000	0.7798	0.6126	0.5437	0.5543
0.3883	2.1	17500	0.8385	0.5948	0.6225	0.5978
0.4011	2.16	18000	0.7755	0.6551	0.5787	0.5915
0.3992	2.22	18500	0.7886	0.5582	0.5519	0.5472
0.393	2.28	19000	0.7660	0.5901	0.5923	0.5889
0.3891	2.34	19500	0.7702	0.5792	0.5331	0.5354
0.4119	2.41	20000	0.8545	0.5406	0.5243	0.5111
0.3981	2.47	20500	0.8641	0.5695	0.5536	0.5364
0.4	2.53	21000	0.8045	0.5988	0.5845	0.5822
0.4059	2.59	21500	0.8023	0.6301	0.5549	0.5696
0.3805	2.65	22000	0.8242	0.5633	0.5363	0.5387
0.4126	2.71	22500	0.8866	0.5630	0.5244	0.5253
0.3959	2.77	23000	0.9228	0.6486	0.5570	0.5716
0.3972	2.83	23500	0.8297	0.6415	0.6336	0.6330
0.3779	2.89	24000	0.8683	0.6023	0.5920	0.5897
0.3951	2.95	24500	0.8628	0.5892	0.5116	0.5125
0.3916	3.01	25000	0.9203	0.6305	0.5026	0.5024
0.3524	3.07	25500	0.9825	0.6089	0.5039	0.5011
0.3332	3.13	26000	0.8755	0.5980	0.5712	0.5814
0.3517	3.19	26500	0.9922	0.6701	0.5941	0.6181
0.3534	3.25	27000	0.9573	0.5653	0.5175	0.5243
0.3544	3.31	27500	0.9827	0.5739	0.5531	0.5551
0.3526	3.37	28000	0.9517	0.6019	0.4737	0.4657
0.3448	3.43	28500	0.9559	0.5744	0.5138	0.5232
0.3662	3.49	29000	0.8470	0.6417	0.6176	0.6173
0.3502	3.55	29500	0.8524	0.6606	0.5776	0.5912
0.3733	3.61	30000	0.9210	0.5578	0.5555	0.5466
0.3424	3.67	30500	0.9295	0.5863	0.6100	0.5809
0.3591	3.73	31000	0.9707	0.5828	0.4769	0.4588
0.3634	3.79	31500	0.8524	0.6136	0.5681	0.5752

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Text Classification
Library: transformers
Type: xlm-roberta
Created: May 16, 2024
Updated: Jun 5, 2024