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---
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
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