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