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---
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 |