tensorboard safetensors distilbert generated_from_trainer dataset:generator base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index region:us
---
base_model: distilbert-base-uncased
datasets:
- generator
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: finetuning-Amazon-sentiment-model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9166666666666666
      name: Accuracy
    - type: f1
      value: 0.9169435215946843
      name: F1
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/karta1535927-no/huggingface/runs/97as2nex)
# finetuning-Amazon-sentiment-model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2228
- Accuracy: 0.9167
- F1: 0.9169

## 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: 2

### Training results



### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
