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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: twitter_sentiment_small_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/abson-/twitter_sentiment_small/runs/j4w9p70i)
# twitter_sentiment_small_3
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4820
- Accuracy: 0.811
- F1-score: 0.7869
- Precision: 0.8290
- Recall: 0.7489
## Model description
More information needed
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.8,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 0.6739 | 0.0889 | 100 | 0.5831 | 0.742 | 0.6742 | 0.8190 | 0.5730 |
| 0.5084 | 0.1778 | 200 | 0.4898 | 0.787 | 0.7560 | 0.8108 | 0.7082 |
| 0.4133 | 0.2667 | 300 | 0.4619 | 0.801 | 0.7881 | 0.7822 | 0.7940 |
| 0.4277 | 0.3556 | 400 | 0.4401 | 0.797 | 0.768 | 0.8215 | 0.7210 |
| 0.4066 | 0.4444 | 500 | 0.4813 | 0.811 | 0.8062 | 0.7721 | 0.8433 |
| 0.4091 | 0.5333 | 600 | 0.4396 | 0.808 | 0.7876 | 0.8128 | 0.7639 |
| 0.3873 | 0.6222 | 700 | 0.4338 | 0.804 | 0.7971 | 0.77 | 0.8262 |
| 0.3851 | 0.7111 | 800 | 0.3983 | 0.803 | 0.7898 | 0.7856 | 0.7940 |
| 0.4003 | 0.8 | 900 | 0.4140 | 0.806 | 0.8012 | 0.7667 | 0.8391 |
| 0.3738 | 0.8889 | 1000 | 0.4047 | 0.81 | 0.8041 | 0.7738 | 0.8369 |
| 0.3718 | 0.9778 | 1100 | 0.4227 | 0.815 | 0.8083 | 0.7816 | 0.8369 |
| 0.3235 | 1.0667 | 1200 | 0.4731 | 0.815 | 0.8103 | 0.7760 | 0.8476 |
| 0.3132 | 1.1556 | 1300 | 0.4716 | 0.815 | 0.8099 | 0.7771 | 0.8455 |
| 0.3026 | 1.2444 | 1400 | 0.4650 | 0.811 | 0.8046 | 0.7764 | 0.8348 |
| 0.2918 | 1.3333 | 1500 | 0.4641 | 0.812 | 0.8070 | 0.7736 | 0.8433 |
| 0.296 | 1.4222 | 1600 | 0.4820 | 0.811 | 0.7869 | 0.8290 | 0.7489 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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