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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_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<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/c6phae8a)
# twitter_sentiment_small_4
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: 0.4844
- Accuracy: 0.824
- F1-score: 0.8057
- Precision: 0.8295
- Recall: 0.7833
## 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: 2e-05
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 0.643 | 0.0889 | 100 | 0.5191 | 0.774 | 0.7466 | 0.7817 | 0.7146 |
| 0.4779 | 0.1778 | 200 | 0.4895 | 0.787 | 0.7677 | 0.7805 | 0.7554 |
| 0.4069 | 0.2667 | 300 | 0.4630 | 0.795 | 0.7745 | 0.7946 | 0.7554 |
| 0.4215 | 0.3556 | 400 | 0.4562 | 0.8 | 0.7669 | 0.8393 | 0.7060 |
| 0.4187 | 0.4444 | 500 | 0.4350 | 0.807 | 0.7992 | 0.7758 | 0.8240 |
| 0.4197 | 0.5333 | 600 | 0.4497 | 0.806 | 0.7785 | 0.8317 | 0.7318 |
| 0.4034 | 0.6222 | 700 | 0.4335 | 0.817 | 0.8111 | 0.7813 | 0.8433 |
| 0.4058 | 0.7111 | 800 | 0.4231 | 0.804 | 0.7996 | 0.7637 | 0.8391 |
| 0.4044 | 0.8 | 900 | 0.4404 | 0.805 | 0.8056 | 0.7523 | 0.8670 |
| 0.3678 | 0.8889 | 1000 | 0.4000 | 0.815 | 0.8095 | 0.7782 | 0.8433 |
| 0.3791 | 0.9778 | 1100 | 0.4451 | 0.814 | 0.814 | 0.7622 | 0.8734 |
| 0.3109 | 1.0667 | 1200 | 0.5034 | 0.817 | 0.8039 | 0.8030 | 0.8047 |
| 0.2999 | 1.1556 | 1300 | 0.4740 | 0.812 | 0.8105 | 0.7643 | 0.8627 |
| 0.2902 | 1.2444 | 1400 | 0.4517 | 0.825 | 0.8066 | 0.8314 | 0.7833 |
| 0.2664 | 1.3333 | 1500 | 0.4646 | 0.83 | 0.8225 | 0.8008 | 0.8455 |
| 0.2826 | 1.4222 | 1600 | 0.4844 | 0.824 | 0.8057 | 0.8295 | 0.7833 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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