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---
license: mit
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_roBERTa_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.8933333333333333
---
<!-- 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. -->
# Sentiment140_roBERTa_5E
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4796
- Accuracy: 0.8933
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.699 | 0.08 | 50 | 0.6734 | 0.5467 |
| 0.6099 | 0.16 | 100 | 0.4322 | 0.8 |
| 0.4906 | 0.24 | 150 | 0.3861 | 0.84 |
| 0.4652 | 0.32 | 200 | 0.4288 | 0.7933 |
| 0.4874 | 0.4 | 250 | 0.3872 | 0.84 |
| 0.4735 | 0.48 | 300 | 0.3401 | 0.8667 |
| 0.3909 | 0.56 | 350 | 0.3484 | 0.84 |
| 0.4277 | 0.64 | 400 | 0.3207 | 0.88 |
| 0.3894 | 0.72 | 450 | 0.3310 | 0.8733 |
| 0.4523 | 0.8 | 500 | 0.3389 | 0.8667 |
| 0.4087 | 0.88 | 550 | 0.3515 | 0.8467 |
| 0.3973 | 0.96 | 600 | 0.3513 | 0.8467 |
| 0.4016 | 1.04 | 650 | 0.3501 | 0.8667 |
| 0.3613 | 1.12 | 700 | 0.3327 | 0.8667 |
| 0.343 | 1.2 | 750 | 0.3518 | 0.86 |
| 0.314 | 1.28 | 800 | 0.3555 | 0.88 |
| 0.3407 | 1.36 | 850 | 0.3849 | 0.86 |
| 0.2944 | 1.44 | 900 | 0.3576 | 0.8667 |
| 0.3267 | 1.52 | 950 | 0.3461 | 0.8733 |
| 0.3251 | 1.6 | 1000 | 0.3411 | 0.8667 |
| 0.321 | 1.68 | 1050 | 0.3371 | 0.88 |
| 0.3057 | 1.76 | 1100 | 0.3322 | 0.88 |
| 0.3335 | 1.84 | 1150 | 0.3106 | 0.8667 |
| 0.3363 | 1.92 | 1200 | 0.3158 | 0.8933 |
| 0.2972 | 2.0 | 1250 | 0.3122 | 0.88 |
| 0.2453 | 2.08 | 1300 | 0.3327 | 0.8867 |
| 0.2467 | 2.16 | 1350 | 0.3767 | 0.8667 |
| 0.273 | 2.24 | 1400 | 0.3549 | 0.8667 |
| 0.2672 | 2.32 | 1450 | 0.3470 | 0.88 |
| 0.2352 | 2.4 | 1500 | 0.4092 | 0.8667 |
| 0.2763 | 2.48 | 1550 | 0.3472 | 0.9 |
| 0.2858 | 2.56 | 1600 | 0.3440 | 0.9 |
| 0.2206 | 2.64 | 1650 | 0.3770 | 0.88 |
| 0.2928 | 2.72 | 1700 | 0.3280 | 0.8867 |
| 0.2478 | 2.8 | 1750 | 0.3426 | 0.8867 |
| 0.2362 | 2.88 | 1800 | 0.3578 | 0.8933 |
| 0.2107 | 2.96 | 1850 | 0.3986 | 0.8933 |
| 0.2191 | 3.04 | 1900 | 0.3819 | 0.8933 |
| 0.2267 | 3.12 | 1950 | 0.4047 | 0.8867 |
| 0.2076 | 3.2 | 2000 | 0.4303 | 0.8867 |
| 0.1868 | 3.28 | 2050 | 0.4385 | 0.8933 |
| 0.2239 | 3.36 | 2100 | 0.4175 | 0.8933 |
| 0.2082 | 3.44 | 2150 | 0.4142 | 0.8933 |
| 0.2423 | 3.52 | 2200 | 0.4002 | 0.8867 |
| 0.1878 | 3.6 | 2250 | 0.4662 | 0.88 |
| 0.1892 | 3.68 | 2300 | 0.4783 | 0.88 |
| 0.2259 | 3.76 | 2350 | 0.4487 | 0.88 |
| 0.1859 | 3.84 | 2400 | 0.4456 | 0.8933 |
| 0.2042 | 3.92 | 2450 | 0.4468 | 0.8933 |
| 0.2096 | 4.0 | 2500 | 0.4153 | 0.8867 |
| 0.178 | 4.08 | 2550 | 0.4100 | 0.8933 |
| 0.1621 | 4.16 | 2600 | 0.4292 | 0.8933 |
| 0.1682 | 4.24 | 2650 | 0.4602 | 0.8933 |
| 0.1813 | 4.32 | 2700 | 0.4680 | 0.8933 |
| 0.2033 | 4.4 | 2750 | 0.4735 | 0.8933 |
| 0.1662 | 4.48 | 2800 | 0.4750 | 0.88 |
| 0.1686 | 4.56 | 2850 | 0.4830 | 0.8933 |
| 0.1603 | 4.64 | 2900 | 0.4909 | 0.8933 |
| 0.148 | 4.72 | 2950 | 0.4784 | 0.8933 |
| 0.162 | 4.8 | 3000 | 0.4750 | 0.8867 |
| 0.153 | 4.88 | 3050 | 0.4759 | 0.8867 |
| 0.1657 | 4.96 | 3100 | 0.4796 | 0.8933 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
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