End of training

README.md

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 ---
-{}
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: tuning
+  results: []
 ---
 
-# omaressamrme/tuning
-
-Fine-tuned DistilBERT for sentiment analysis on the IMDb dataset.
-
-## Training setup
-- Base model: distilbert-base-uncased
-- Dataset: IMDb (train/test)
-- Epochs: 1
-- Learning rate: 2e-05
-- Train batch size: 16
-- Eval batch size: 32
-- Max train samples: 1000
-- Max eval samples: 500
-
-## Usage
-```python
-from transformers import pipeline
-clf = pipeline("text-classification", model="omaressamrme/tuning")
-print(clf("I absolutely loved this movie!"))
-```
-
-## Notes
-- This model and tokenizer were pushed automatically from a local training script.
-- For reproducibility, see the training script in the associated GitHub repo.
+<!-- 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. -->
+
+# tuning
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.7005
+- eval_model_preparation_time: 0.0015
+- eval_accuracy: 0.31
+- eval_f1: 0.0
+- eval_runtime: 26.0619
+- eval_samples_per_second: 19.185
+- eval_steps_per_second: 0.614
+- step: 0
+
+## 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: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1
+
+### Framework versions
+
+- Transformers 4.57.0
+- Pytorch 2.8.0+cpu
+- Datasets 4.2.0
+- Tokenizers 0.22.1

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