---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta_large_len256
  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. -->

# roberta_large_len256

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: 0.3063
- Accuracy: 0.9503
- F1: 0.9503
- Precision: 0.9503
- Recall: 0.9503

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3137        | 1.0   | 4000  | 0.3495          | 0.9193   | 0.9195 | 0.9217    | 0.9193 |
| 0.1578        | 2.0   | 8000  | 0.2891          | 0.9484   | 0.9484 | 0.9491    | 0.9484 |
| 0.0851        | 3.0   | 12000 | 0.3063          | 0.9503   | 0.9503 | 0.9503    | 0.9503 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.1.2
- Datasets 3.5.1
- Tokenizers 0.21.1