---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-best-finetuned-hopes-fears
  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-best-finetuned-hopes-fears

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3593
- Accuracy: 0.9434
- F1 Weighted: 0.9453
- Precision Fears: 0.7053
- Recall Fears: 0.8171
- F1 Fears: 0.7571
- Precision Hopes: 0.7458
- Recall Hopes: 0.88
- F1 Hopes: 0.8073
- Precision Neither: 0.9795
- Recall Neither: 0.9579
- F1 Neither: 0.9685

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | Precision Fears | Recall Fears | F1 Fears | Precision Hopes | Recall Hopes | F1 Hopes | Precision Neither | Recall Neither | F1 Neither |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:-----------------:|:--------------:|:----------:|
| No log        | 1.0   | 214  | 0.7739          | 0.8930   | 0.8651      | 0.4776          | 0.2602       | 0.3368   | 0.0             | 0.0          | 0.0      | 0.9129            | 0.9876         | 0.9488     |
| 0.8895        | 2.0   | 428  | 0.2800          | 0.8960   | 0.9087      | 0.4736          | 0.9106       | 0.6231   | 0.7417          | 0.89         | 0.8091   | 0.9893            | 0.8949         | 0.9397     |
| 0.2905        | 3.0   | 642  | 0.3252          | 0.9492   | 0.9496      | 0.7879          | 0.7398       | 0.7631   | 0.7143          | 0.95         | 0.8155   | 0.9759            | 0.9691         | 0.9725     |
| 0.2905        | 4.0   | 856  | 0.2671          | 0.9281   | 0.9340      | 0.5813          | 0.8862       | 0.7021   | 0.8018          | 0.89         | 0.8436   | 0.9869            | 0.9335         | 0.9595     |
| 0.1741        | 5.0   | 1070 | 0.3593          | 0.9434   | 0.9453      | 0.7053          | 0.8171       | 0.7571   | 0.7458          | 0.88         | 0.8073   | 0.9795            | 0.9579         | 0.9685     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1