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---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deeva-modcat-seqclass-deberta-v1
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. -->
# deeva-modcat-seqclass-deberta-v1
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6435
- Accuracy: 0.7161
- F1: 0.2922
- Precision: 0.1808
- Recall: 0.7619
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 0.18 | 2 | 0.7148 | 0.4139 | 0.0476 | 0.0272 | 0.1905 |
| No log | 0.36 | 4 | 0.7027 | 0.4835 | 0.0408 | 0.0238 | 0.1429 |
| No log | 0.55 | 6 | 0.6917 | 0.5586 | 0.0474 | 0.0284 | 0.1429 |
| No log | 0.73 | 8 | 0.6817 | 0.5604 | 0.0476 | 0.0286 | 0.1429 |
| No log | 0.91 | 10 | 0.6727 | 0.5623 | 0.0478 | 0.0287 | 0.1429 |
| No log | 1.09 | 12 | 0.6648 | 0.6374 | 0.0571 | 0.0357 | 0.1429 |
| No log | 1.27 | 14 | 0.6578 | 0.6374 | 0.0571 | 0.0357 | 0.1429 |
| No log | 1.45 | 16 | 0.6521 | 0.6355 | 0.0569 | 0.0355 | 0.1429 |
| No log | 1.64 | 18 | 0.6477 | 0.6392 | 0.1005 | 0.0621 | 0.2619 |
| No log | 1.82 | 20 | 0.6448 | 0.7015 | 0.2419 | 0.1503 | 0.6190 |
| No log | 2.0 | 22 | 0.6435 | 0.7161 | 0.2922 | 0.1808 | 0.7619 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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