---
library_name: transformers
license: apache-2.0
base_model: tasksource/deberta-small-long-nli
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: toxicity-scorer
  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. -->

# toxicity-scorer

This model is a fine-tuned version of [tasksource/deberta-small-long-nli](https://huggingface.co./tasksource/deberta-small-long-nli) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1972
- F1: 0.9191
- Accuracy: 0.9205
- Precision: 0.9181
- Recall: 0.9205

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Accuracy | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|:---------:|:------:|
| No log        | 0     | 0     | 0.7648          | 0.5092 | 0.4397   | 0.8431    | 0.4397 |
| 0.2012        | 1.0   | 8816  | 0.1993          | 0.9180 | 0.9200   | 0.9167    | 0.9200 |
| 0.1929        | 2.0   | 17632 | 0.1973          | 0.9192 | 0.9204   | 0.9182    | 0.9204 |
| 0.1991        | 3.0   | 26448 | 0.1972          | 0.9191 | 0.9205   | 0.9181    | 0.9205 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3