---
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