NEGATIVE NEUTRAL POSITIVE
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ecommerce_reviews_with_language_drift
metrics:
- accuracy
- f1
model-index:
- name: distilbert_reviews_with_language_drift
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: ecommerce_reviews_with_language_drift
      type: ecommerce_reviews_with_language_drift
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.818
    - name: F1
      type: f1
      value: 0.8167126877417763
widget:
- text: "Poor quality of fabric and ridiculously tight at chest. It's way too short."
  example_title: "Negative"
- text: "One worked perfectly, but the other one has a slight leak and we end up with water underneath the filter."
  example_title: "Neutral"
- text: "I liked the price most! Nothing to dislike here!"
  example_title: "Positive"
---

<!-- 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. -->

# distilbert_reviews_with_language_drift

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ecommerce_reviews_with_language_drift dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4970
- Accuracy: 0.818
- F1: 0.8167

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.593         | 1.0   | 500  | 0.4723          | 0.799    | 0.7976 |
| 0.3714        | 2.0   | 1000 | 0.4679          | 0.818    | 0.8177 |
| 0.2652        | 3.0   | 1500 | 0.4970          | 0.818    | 0.8167 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
