---
library_name: transformers
base_model: microsoft/deberta-v3-large
tags:
- single_label_classification
- question-answering
- text-classification
- generated_from_trainer
datasets:
- beavertails
metrics:
- accuracy
model-index:
- name: QA-DeBERTa-MeanPooling-binary
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: saiteki-kai/Beavertails-it
      type: beavertails
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.864490800811791
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# QA-DeBERTa-MeanPooling-binary

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the saiteki-kai/Beavertails-it dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3246
- Accuracy: 0.8645
- Unsafe Precision: 0.8883
- Unsafe Recall: 0.8653
- Unsafe F1: 0.8766
- Unsafe Fpr: 0.1366
- Unsafe Aucpr: 0.9567
- Safe Precision: 0.8363
- Safe Recall: 0.8634
- Safe F1: 0.8497
- Safe Fpr: 0.1347
- Safe Aucpr: 0.9241

## 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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | Unsafe Precision | Unsafe Recall | Unsafe F1 | Unsafe Fpr | Unsafe Aucpr | Safe Precision | Safe Recall | Safe F1 | Safe Fpr | Safe Aucpr |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:----------:|:------------:|:--------------:|:-----------:|:-------:|:--------:|:----------:|
| 0.3568        | 0.3001 | 5073  | 0.3409          | 0.8500   | 0.8498           | 0.8871        | 0.8681    | 0.1966     | 0.9480       | 0.8501         | 0.8034      | 0.8261  | 0.1129   | 0.9069     |
| 0.3161        | 0.6001 | 10146 | 0.3243          | 0.8592   | 0.8766           | 0.8693        | 0.8729    | 0.1536     | 0.9519       | 0.8377         | 0.8464      | 0.8420  | 0.1307   | 0.9135     |
| 0.304         | 0.9002 | 15219 | 0.3204          | 0.8609   | 0.8710           | 0.8805        | 0.8757    | 0.1636     | 0.9537       | 0.8480         | 0.8364      | 0.8422  | 0.1195   | 0.9169     |
| 0.3128        | 1.2002 | 20292 | 0.3311          | 0.8627   | 0.8932           | 0.8556        | 0.8740    | 0.1283     | 0.9532       | 0.8279         | 0.8717      | 0.8493  | 0.1444   | 0.9191     |
| 0.3198        | 1.5003 | 25365 | 0.3146          | 0.8624   | 0.8815           | 0.8695        | 0.8755    | 0.1466     | 0.9555       | 0.8391         | 0.8534      | 0.8462  | 0.1305   | 0.9225     |
| 0.2852        | 1.8003 | 30438 | 0.3174          | 0.8647   | 0.8937           | 0.8591        | 0.8761    | 0.1282     | 0.9568       | 0.8314         | 0.8718      | 0.8511  | 0.1409   | 0.9240     |
| 0.2694        | 2.1004 | 35511 | 0.3260          | 0.8642   | 0.8895           | 0.8633        | 0.8762    | 0.1346     | 0.9566       | 0.8346         | 0.8654      | 0.8497  | 0.1367   | 0.9231     |
| 0.3003        | 2.4004 | 40584 | 0.3265          | 0.8640   | 0.8850           | 0.8683        | 0.8766    | 0.1415     | 0.9568       | 0.8386         | 0.8585      | 0.8484  | 0.1317   | 0.9239     |
| 0.2723        | 2.7005 | 45657 | 0.3246          | 0.8644   | 0.8882           | 0.8653        | 0.8766    | 0.1366     | 0.9567       | 0.8363         | 0.8634      | 0.8496  | 0.1347   | 0.9241     |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1