---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-v2
  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-v2

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
-  F1 Micro: 0.9100
-  F1 Macro: 0.8958
-  Precision Micro: 0.9072
-  Recall Micro: 0.9129

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- 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: linear
- lr_scheduler_warmup_steps: 2096
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |  F1 Micro |  F1 Macro |  Precision Micro |  Recall Micro |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:----------------:|:-------------:|
| 0.4516        | 1.0   | 5240  | 0.2242          | 0.8697    | 0.8363    | 0.8975           | 0.8436        |
| 0.4487        | 2.0   | 10480 | 0.2218          | 0.8862    | 0.8683    | 0.8939           | 0.8786        |
| 0.4390        | 3.0   | 15720 | 0.2207          | 0.8997    | 0.8836    | 0.8985           | 0.9010        |
| 0.4409        | 4.0   | 20960 | 0.2200          | 0.9072    | 0.8929    | 0.9069           | 0.9075        |


### Framework versions

- Transformers 5.11.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2