| | --- |
| | base_model: distilbert-base-uncased |
| | datasets: |
| | - arrow |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | - sentiment-classification |
| | - LLM |
| | model-index: |
| | - name: cls_distilbert_model |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # cls_distilbert_model |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the arrow dataset. |
| | It achieves the following results on the evaluation set: |
| | - eval_loss: 0.4205 |
| | - eval_accuracy: 0.8218 |
| | - eval_f1: 0.8203 |
| | - eval_precision: 0.8326 |
| | - eval_recall: 0.8218 |
| | - eval_runtime: 1.4638 |
| | - eval_samples_per_second: 728.218 |
| | - eval_steps_per_second: 45.77 |
| | - epoch: 1.0 |
| | - step: 534 |
| |
|
| | ## Model description |
| |
|
| | Model is used to classify the sentiment POSITIVE or NEGATIVE for given sample inout textx |
| |
|
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.41.2 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.19.1 |
| | - Tokenizers 0.19.0 |
