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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
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2787 | 0.49 | 100 | 1.1127 | 0.4866 |
| 1.089 | 0.98 | 200 | 0.9668 | 0.7139 |
| 0.9134 | 1.47 | 300 | 0.8720 | 0.7834 |
| 0.8618 | 1.96 | 400 | 0.7726 | 0.7941 |
| 0.686 | 2.45 | 500 | 0.7337 | 0.8209 |
| 0.6333 | 2.94 | 600 | 0.7350 | 0.8235 |
| 0.5765 | 3.43 | 700 | 0.7561 | 0.8235 |
| 0.5502 | 3.92 | 800 | 0.7273 | 0.8476 |
| 0.5049 | 4.41 | 900 | 0.8137 | 0.8102 |
| 0.4695 | 4.9 | 1000 | 0.7581 | 0.8289 |
| 0.4657 | 5.39 | 1100 | 0.8404 | 0.8048 |
| 0.4549 | 5.88 | 1200 | 0.7800 | 0.8369 |
| 0.4305 | 6.37 | 1300 | 0.8575 | 0.8235 |
| 0.4209 | 6.86 | 1400 | 0.8572 | 0.8102 |
| 0.3983 | 7.35 | 1500 | 0.8392 | 0.8316 |
| 0.4139 | 7.84 | 1600 | 0.8152 | 0.8209 |
| 0.393 | 8.33 | 1700 | 0.8261 | 0.8289 |
| 0.3979 | 8.82 | 1800 | 0.8328 | 0.8235 |
| 0.3928 | 9.31 | 1900 | 0.8364 | 0.8209 |
| 0.3848 | 9.8 | 2000 | 0.8322 | 0.8235 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
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