---
license: mit
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: rtm_roBERTa_5E
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: rotten_tomatoes
      type: rotten_tomatoes
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8666666666666667
---

<!-- 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. -->

# rtm_roBERTa_5E

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6545
- Accuracy: 0.8667

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6955        | 0.09  | 50   | 0.6752          | 0.7867   |
| 0.5362        | 0.19  | 100  | 0.4314          | 0.8333   |
| 0.4065        | 0.28  | 150  | 0.4476          | 0.8533   |
| 0.3563        | 0.37  | 200  | 0.3454          | 0.8467   |
| 0.3729        | 0.47  | 250  | 0.3421          | 0.86     |
| 0.3355        | 0.56  | 300  | 0.3253          | 0.8467   |
| 0.338         | 0.66  | 350  | 0.3859          | 0.8733   |
| 0.2875        | 0.75  | 400  | 0.3537          | 0.8533   |
| 0.3477        | 0.84  | 450  | 0.3636          | 0.8467   |
| 0.3259        | 0.94  | 500  | 0.3115          | 0.88     |
| 0.3204        | 1.03  | 550  | 0.4295          | 0.8333   |
| 0.2673        | 1.12  | 600  | 0.3369          | 0.88     |
| 0.2479        | 1.22  | 650  | 0.3620          | 0.8667   |
| 0.2821        | 1.31  | 700  | 0.3582          | 0.8733   |
| 0.2355        | 1.4   | 750  | 0.3130          | 0.8867   |
| 0.2357        | 1.5   | 800  | 0.3229          | 0.86     |
| 0.2725        | 1.59  | 850  | 0.3035          | 0.88     |
| 0.2425        | 1.69  | 900  | 0.3146          | 0.8533   |
| 0.1977        | 1.78  | 950  | 0.4079          | 0.86     |
| 0.2557        | 1.87  | 1000 | 0.4132          | 0.8733   |
| 0.2395        | 1.97  | 1050 | 0.3336          | 0.86     |
| 0.1951        | 2.06  | 1100 | 0.5068          | 0.84     |
| 0.1631        | 2.15  | 1150 | 0.5209          | 0.8867   |
| 0.2192        | 2.25  | 1200 | 0.4766          | 0.8733   |
| 0.1725        | 2.34  | 1250 | 0.3962          | 0.8667   |
| 0.2215        | 2.43  | 1300 | 0.4133          | 0.8867   |
| 0.1602        | 2.53  | 1350 | 0.5564          | 0.8533   |
| 0.1986        | 2.62  | 1400 | 0.5826          | 0.86     |
| 0.1972        | 2.72  | 1450 | 0.5412          | 0.8667   |
| 0.2299        | 2.81  | 1500 | 0.4636          | 0.8733   |
| 0.2028        | 2.9   | 1550 | 0.5096          | 0.8667   |
| 0.2591        | 3.0   | 1600 | 0.3790          | 0.8467   |
| 0.1197        | 3.09  | 1650 | 0.5704          | 0.8467   |
| 0.174         | 3.18  | 1700 | 0.5904          | 0.8467   |
| 0.1499        | 3.28  | 1750 | 0.6066          | 0.86     |
| 0.1687        | 3.37  | 1800 | 0.6353          | 0.8533   |
| 0.1463        | 3.46  | 1850 | 0.6434          | 0.8467   |
| 0.1373        | 3.56  | 1900 | 0.6507          | 0.8533   |
| 0.1339        | 3.65  | 1950 | 0.6014          | 0.86     |
| 0.1488        | 3.75  | 2000 | 0.7245          | 0.84     |
| 0.1725        | 3.84  | 2050 | 0.6214          | 0.86     |
| 0.1443        | 3.93  | 2100 | 0.6446          | 0.8533   |
| 0.1619        | 4.03  | 2150 | 0.6223          | 0.8533   |
| 0.1153        | 4.12  | 2200 | 0.6579          | 0.8333   |
| 0.1159        | 4.21  | 2250 | 0.6760          | 0.8667   |
| 0.0948        | 4.31  | 2300 | 0.7172          | 0.8467   |
| 0.1373        | 4.4   | 2350 | 0.7346          | 0.8467   |
| 0.1463        | 4.49  | 2400 | 0.6453          | 0.8533   |
| 0.0758        | 4.59  | 2450 | 0.6579          | 0.86     |
| 0.16          | 4.68  | 2500 | 0.6556          | 0.8667   |
| 0.112         | 4.78  | 2550 | 0.6490          | 0.88     |
| 0.1151        | 4.87  | 2600 | 0.6525          | 0.8667   |
| 0.2152        | 4.96  | 2650 | 0.6545          | 0.8667   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2