| --- |
| 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 |
| |
