deberta-v3-large-hate

Elron/deberta-v3-large-hate

deberta-v3-large-hate is a text classification model from Elron. 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.

This model is a fine-tuned version of microsoft/deberta-v3-large on an tweeteval dataset.

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Jun 22, 2022
Updated: Jun 22, 2022
SHA: db034e2f

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Model card

From the Hugging Face model README

deberta-v3-large-sentiment

This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet_eval dataset.

Model description

Test set results:

Model	Emotion	Hate	Irony	Offensive	Sentiment
deberta-v3-large	86.3	61.3	87.1	86.4	73.9
BERTweet	79.3	-	82.1	79.5	73.4
RoB-RT	79.5	52.3	61.7	80.5	69.3

source:papers_with_code

Intended uses & limitations

Classifying attributes of interest on tweeter like data.

Training and evaluation data

tweet_eval dataset.

Training procedure

Fine tuned and evaluated with run_glue.py

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7e-06
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
lr_scheduler_warmup_steps: 50
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6362	0.18	100	0.5481	0.7197
0.4264	0.36	200	0.4550	0.8008
0.4174	0.53	300	0.4524	0.7868
0.4197	0.71	400	0.4586	0.7918
0.3819	0.89	500	0.4368	0.8078
0.3558	1.07	600	0.4525	0.8068
0.2982	1.24	700	0.4999	0.7928
0.2885	1.42	800	0.5129	0.8108
0.253	1.6	900	0.5873	0.8208
0.3354	1.78	1000	0.4244	0.8178
0.3083	1.95	1100	0.4853	0.8058
0.2301	2.13	1200	0.7209	0.8018
0.2167	2.31	1300	0.8090	0.7778
0.1863	2.49	1400	0.6812	0.8038
0.2181	2.66	1500	0.6958	0.8138
0.2159	2.84	1600	0.6315	0.8118
0.1828	3.02	1700	0.7173	0.8138
0.1287	3.2	1800	0.9081	0.8018
0.1711	3.37	1900	0.8858	0.8068
0.1598	3.55	2000	0.7878	0.8028
0.1467	3.73	2100	0.9003	0.7948
0.127	3.91	2200	0.9066	0.8048
0.1134	4.09	2300	0.9646	0.8118
0.1017	4.26	2400	0.9778	0.8048
0.085	4.44	2500	1.0529	0.8088
0.0996	4.62	2600	1.0082	0.8058
0.1054	4.8	2700	0.9698	0.8108
0.1375	4.97	2800	0.9334	0.8048
0.0487	5.15	2900	1.1273	0.8108
0.0611	5.33	3000	1.1528	0.8058
0.0668	5.51	3100	1.0148	0.8118
0.0582	5.68	3200	1.1333	0.8108
0.0869	5.86	3300	1.0607	0.8088
0.0623	6.04	3400	1.1880	0.8068
0.0317	6.22	3500	1.2836	0.8008
0.0546	6.39	3600	1.2148	0.8058
0.0486	6.57	3700	1.3348	0.8008
0.0332	6.75	3800	1.3734	0.8018
0.051	6.93	3900	1.2966	0.7978
0.0217	7.1	4000	1.3853	0.8048
0.0109	7.28	4100	1.4803	0.8068
0.0345	7.46	4200	1.4906	0.7998
0.0365	7.64	4300	1.4347	0.8028
0.0265	7.82	4400	1.3977	0.8128
0.0257	7.99	4500	1.3705	0.8108
0.0036	8.17	4600	1.4353	0.8168
0.0269	8.35	4700	1.4826	0.8068
0.0231	8.53	4800	1.4811	0.8118
0.0204	8.7	4900	1.5245	0.8028
0.0263	8.88	5000	1.5123	0.8018
0.0138	9.06	5100	1.5113	0.8028
0.0089	9.24	5200	1.5846	0.7978
0.029	9.41	5300	1.5362	0.8008
0.0058	9.59	5400	1.5759	0.8018
0.0084	9.77	5500	1.5679	0.8018
0.0065	9.95	5600	1.5683	0.8028

Framework versions

Transformers 4.20.0.dev0
Pytorch 1.9.0
Datasets 2.2.2
Tokenizers 0.11.6

Task: Text Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Jun 22, 2022
Updated: Jun 22, 2022