---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wikidyk-scope-clf-deberta-v3-large-semantic_3_clusters
  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. -->

# wikidyk-scope-clf-deberta-v3-large-semantic_3_clusters

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5560
- Accuracy: 0.9132
- F1: 0.7401
- Precision: 0.8592
- Recall: 0.6501

## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0494        | 1.0   | 902  | 0.3398          | 0.8673   | 0.5190 | 0.8346    | 0.3766 |
| 0.0428        | 2.0   | 1804 | 0.3267          | 0.8902   | 0.6328 | 0.8696    | 0.4973 |
| 0.0292        | 3.0   | 2706 | 0.3243          | 0.9071   | 0.7333 | 0.8077    | 0.6714 |
| 0.0208        | 4.0   | 3608 | 0.4366          | 0.8926   | 0.6603 | 0.8284    | 0.5488 |
| 0.0096        | 5.0   | 4510 | 0.4438          | 0.8943   | 0.6829 | 0.7948    | 0.5986 |
| 0.0182        | 6.0   | 5412 | 0.5225          | 0.8950   | 0.6594 | 0.86      | 0.5346 |
| 0.0062        | 7.0   | 6314 | 0.4717          | 0.9078   | 0.7117 | 0.8776    | 0.5986 |
| 0.0021        | 8.0   | 7216 | 0.5226          | 0.9095   | 0.7254 | 0.8571    | 0.6288 |
| 0.0026        | 9.0   | 8118 | 0.5323          | 0.9173   | 0.7528 | 0.8715    | 0.6625 |
| 0.0008        | 10.0  | 9020 | 0.5560          | 0.9132   | 0.7401 | 0.8592    | 0.6501 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1