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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- poem_sentiment
metrics:
- accuracy
model-index:
- name: poem_sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: poem_sentiment
type: poem_sentiment
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8857142857142857
---
<!-- 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. -->
# poem_sentiment
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the poem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- 0: {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19}
- 1: {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17}
- 2: {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69}
- Accuracy: 0.8857
- Macro avg: {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105}
- Weighted avg: {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105}
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | 0 | 1 | 2 | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|
| 1.0922 | 1.0 | 112 | 0.8825 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 19} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 17} | {'precision': 0.6571428571428571, 'recall': 1.0, 'f1-score': 0.7931034482758621, 'support': 69} | 0.6571 | {'precision': 0.21904761904761905, 'recall': 0.3333333333333333, 'f1-score': 0.26436781609195403, 'support': 105} | {'precision': 0.43183673469387757, 'recall': 0.6571428571428571, 'f1-score': 0.5211822660098522, 'support': 105} |
| 0.6877 | 2.0 | 224 | 0.4747 | {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19} | {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17} | {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69} | 0.8857 | {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105} | {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105} |
| 0.5299 | 3.0 | 336 | 0.6595 | {'precision': 0.8, 'recall': 0.8421052631578947, 'f1-score': 0.8205128205128205, 'support': 19} | {'precision': 1.0, 'recall': 0.4117647058823529, 'f1-score': 0.5833333333333334, 'support': 17} | {'precision': 0.8461538461538461, 'recall': 0.9565217391304348, 'f1-score': 0.8979591836734695, 'support': 69} | 0.8476 | {'precision': 0.882051282051282, 'recall': 0.7367972360568942, 'f1-score': 0.7672684458398744, 'support': 105} | {'precision': 0.8627106227106227, 'recall': 0.8476190476190476, 'f1-score': 0.8330056564750442, 'support': 105} |
| 0.9027 | 4.0 | 448 | 0.5981 | {'precision': 1.0, 'recall': 0.7368421052631579, 'f1-score': 0.8484848484848484, 'support': 19} | {'precision': 0.7333333333333333, 'recall': 0.6470588235294118, 'f1-score': 0.6875, 'support': 17} | {'precision': 0.868421052631579, 'recall': 0.9565217391304348, 'f1-score': 0.9103448275862069, 'support': 69} | 0.8667 | {'precision': 0.867251461988304, 'recall': 0.7801408893076681, 'f1-score': 0.8154432253570185, 'support': 105} | {'precision': 0.870359231411863, 'recall': 0.8666666666666667, 'f1-score': 0.863071478330099, 'support': 105} |
| 0.4588 | 5.0 | 560 | 0.7815 | {'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1-score': 0.8292682926829269, 'support': 19} | {'precision': 0.6470588235294118, 'recall': 0.6470588235294118, 'f1-score': 0.6470588235294118, 'support': 17} | {'precision': 0.8939393939393939, 'recall': 0.855072463768116, 'f1-score': 0.8740740740740741, 'support': 69} | 0.8286 | {'precision': 0.7712418300653595, 'recall': 0.7989560431342637, 'f1-score': 0.7834670634288043, 'support': 105} | {'precision': 0.832034632034632, 'recall': 0.8285714285714286, 'f1-score': 0.8292115111627308, 'support': 105} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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