BaxterAI/finetuning-sentiment-model-3000-samples

1---2license: apache-2.03tags:4- generated_from_trainer5datasets:6- amazon_polarity7metrics:8- accuracy9- f110model-index:11- name: finetuning-sentiment-model-3000-samples12  results:13  - task:14      name: Text Classification15      type: text-classification16    dataset:17      name: amazon_polarity18      type: amazon_polarity19      args: amazon_polarity20    metrics:21    - name: Accuracy22      type: accuracy23      value: 0.922524    - name: F125      type: f126      value: 0.924081632653061227---28 29<!-- This model card has been generated automatically according to the information the Trainer had access to. You30should probably proofread and complete it, then remove this comment. -->31 32# finetuning-sentiment-model-3000-samples33 34This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset.35It achieves the following results on the evaluation set:36- Loss: 0.817037- Accuracy: 0.922538- F1: 0.924139 40## Model description41 42More information needed43 44## Intended uses & limitations45 46More information needed47 48## Training and evaluation data49 50More information needed51 52## Training procedure53 54### Training hyperparameters55 56The following hyperparameters were used during training:57- learning_rate: 3e-0558- train_batch_size: 1659- eval_batch_size: 1660- seed: 4261- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0862- lr_scheduler_type: linear63- num_epochs: 2064 65### Training results66 67 68 69### Framework versions70 71- Transformers 4.19.272- Pytorch 1.11.0+cu11373- Datasets 2.2.274- Tokenizers 0.12.175