FFZG-cleopatra/M2SA-text-only

Model

stars

commits

repos using this model

linked in READMEs

Jun 5, 2024

updated

endpoints_compatible

generated_from_trainer

safetensors

text-classification

text-embeddings-inference

transformers

xlm-roberta

Browse cluster: RoBERTa Text Classification & NLP →

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

Contributors

thak123

3 commits

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Used in code

texttechnologylab/duui-uima

Docker images for use with DUUI

1 file

Linked in READMEs

cleopatra-itn/M2SA-multimodal-multilingual-sentiment-analysis

Official repository for the paper "M2SA: Multimodal and Multilingual Model for Sentiment Analysis…

README badge

FFZG-cleopatra/M2SA-text-only

Model

stars

commits

repos using this model

linked in READMEs

Jun 5, 2024

updated

endpoints_compatible

generated_from_trainer

safetensors

text-classification

text-embeddings-inference

transformers

xlm-roberta

Browse cluster: RoBERTa Text Classification & NLP →

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