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	---
	license: mit
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: deberta-v3-large
	results: []
	---

	# deberta-v3-large-sentiment

	This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/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](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)


	## Intended uses & limitations

	Classifying attributes of interest on tweeter like data.

	## Training and evaluation data

	[tweet_eval](https://huggingface.co/datasets/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

	---
	license: mit
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: deberta-v3-large
	results: []
	---

	# deberta-v3-large-sentiment

	This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/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](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)


	## Intended uses & limitations

	Classifying attributes of interest on tweeter like data.

	## Training and evaluation data

	[tweet_eval](https://huggingface.co/datasets/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