---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: rtm_DistilBERT_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: 0.82
---

<!-- 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_DistilBERT_5E

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6835
- Accuracy: 0.82

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6822        | 0.09  | 50   | 0.6391          | 0.76     |
| 0.5531        | 0.19  | 100  | 0.4684          | 0.7667   |
| 0.4546        | 0.28  | 150  | 0.4479          | 0.7733   |
| 0.4495        | 0.37  | 200  | 0.3953          | 0.8067   |
| 0.4239        | 0.47  | 250  | 0.4211          | 0.7933   |
| 0.3951        | 0.56  | 300  | 0.4126          | 0.7933   |
| 0.3861        | 0.66  | 350  | 0.3950          | 0.7933   |
| 0.4108        | 0.75  | 400  | 0.4091          | 0.82     |
| 0.3778        | 0.84  | 450  | 0.4107          | 0.7933   |
| 0.3627        | 0.94  | 500  | 0.4203          | 0.7933   |
| 0.3648        | 1.03  | 550  | 0.4190          | 0.8      |
| 0.2899        | 1.12  | 600  | 0.4436          | 0.8      |
| 0.2637        | 1.22  | 650  | 0.4504          | 0.82     |
| 0.2885        | 1.31  | 700  | 0.4406          | 0.82     |
| 0.3226        | 1.4   | 750  | 0.4398          | 0.8333   |
| 0.3147        | 1.5   | 800  | 0.4239          | 0.82     |
| 0.2937        | 1.59  | 850  | 0.4227          | 0.8133   |
| 0.3149        | 1.69  | 900  | 0.3791          | 0.82     |
| 0.3227        | 1.78  | 950  | 0.3888          | 0.8133   |
| 0.2727        | 1.87  | 1000 | 0.4215          | 0.82     |
| 0.2722        | 1.97  | 1050 | 0.4099          | 0.8333   |
| 0.1908        | 2.06  | 1100 | 0.4595          | 0.82     |
| 0.2276        | 2.15  | 1150 | 0.4572          | 0.84     |
| 0.2239        | 2.25  | 1200 | 0.4545          | 0.8333   |
| 0.1986        | 2.34  | 1250 | 0.4895          | 0.82     |
| 0.2388        | 2.43  | 1300 | 0.4352          | 0.86     |
| 0.1901        | 2.53  | 1350 | 0.4806          | 0.84     |
| 0.2227        | 2.62  | 1400 | 0.5473          | 0.8067   |
| 0.2221        | 2.72  | 1450 | 0.5010          | 0.84     |
| 0.1955        | 2.81  | 1500 | 0.5315          | 0.8267   |
| 0.2114        | 2.9   | 1550 | 0.5410          | 0.8133   |
| 0.1827        | 3.0   | 1600 | 0.5721          | 0.8133   |
| 0.1527        | 3.09  | 1650 | 0.5616          | 0.8133   |
| 0.1464        | 3.18  | 1700 | 0.5935          | 0.8067   |
| 0.135         | 3.28  | 1750 | 0.6145          | 0.82     |
| 0.1668        | 3.37  | 1800 | 0.6901          | 0.8067   |
| 0.1702        | 3.46  | 1850 | 0.6067          | 0.8133   |
| 0.1738        | 3.56  | 1900 | 0.5981          | 0.82     |
| 0.1506        | 3.65  | 1950 | 0.6073          | 0.8267   |
| 0.1584        | 3.75  | 2000 | 0.6549          | 0.8133   |
| 0.1698        | 3.84  | 2050 | 0.6660          | 0.8267   |
| 0.1626        | 3.93  | 2100 | 0.6645          | 0.8267   |
| 0.1483        | 4.03  | 2150 | 0.6497          | 0.82     |
| 0.1342        | 4.12  | 2200 | 0.6643          | 0.82     |
| 0.1064        | 4.21  | 2250 | 0.6775          | 0.82     |
| 0.1302        | 4.31  | 2300 | 0.6876          | 0.82     |
| 0.1847        | 4.4   | 2350 | 0.6821          | 0.8133   |
| 0.1055        | 4.49  | 2400 | 0.6928          | 0.8133   |
| 0.1372        | 4.59  | 2450 | 0.6877          | 0.8133   |
| 0.131         | 4.68  | 2500 | 0.6769          | 0.8267   |
| 0.1242        | 4.78  | 2550 | 0.6769          | 0.8267   |
| 0.1289        | 4.87  | 2600 | 0.6810          | 0.82     |
| 0.1488        | 4.96  | 2650 | 0.6835          | 0.82     |


### Framework versions

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