transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:imdb base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- imdb
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: left_padding0model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.92484
      name: Accuracy
---

<!-- 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. -->

# left_padding0model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9248
- Loss: 0.6918

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.2227        | 1.0   | 1563  | 0.9217   | 0.2214          |
| 0.1325        | 2.0   | 3126  | 0.9312   | 0.2340          |
| 0.1433        | 3.0   | 4689  | 0.9273   | 0.2143          |
| 0.1093        | 4.0   | 6252  | 0.9267   | 0.3209          |
| 0.0601        | 5.0   | 7815  | 0.9276   | 0.3734          |
| 0.0497        | 6.0   | 9378  | 0.9174   | 0.4883          |
| 0.0632        | 7.0   | 10941 | 0.9169   | 0.4722          |
| 0.0301        | 8.0   | 12504 | 0.9048   | 0.5964          |
| 0.0292        | 9.0   | 14067 | 0.9261   | 0.4406          |
| 0.0119        | 10.0  | 15630 | 0.9264   | 0.5227          |
| 0.0218        | 11.0  | 17193 | 0.9294   | 0.5665          |
| 0.0161        | 12.0  | 18756 | 0.9276   | 0.5829          |
| 0.0068        | 13.0  | 20319 | 0.928    | 0.5820          |
| 0.0265        | 14.0  | 21882 | 0.9229   | 0.5842          |
| 0.0098        | 15.0  | 23445 | 0.9283   | 0.6034          |
| 0.0081        | 16.0  | 25008 | 0.9251   | 0.6624          |
| 0.0062        | 17.0  | 26571 | 0.9138   | 0.5561          |
| 0.0153        | 18.0  | 28134 | 0.9223   | 0.6722          |
| 0.0213        | 19.0  | 29697 | 0.9233   | 0.6735          |
| 0.0148        | 20.0  | 31260 | 0.9283   | 0.5918          |
| 0.0076        | 21.0  | 32823 | 0.9248   | 0.7200          |
| 0.0088        | 22.0  | 34386 | 0.9221   | 0.6554          |
| 0.0072        | 23.0  | 35949 | 0.9248   | 0.6918          |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
