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README.md

@@ -1,58 +1,72 @@
 ---
-library_name: transformers
+language: ["en"]
 license: apache-2.0
-base_model: distilbert-base-uncased
 tags:
-- generated_from_trainer
+  - text-classification
+  - sentiment-analysis
+  - imdb
+  - distilbert
+pipeline_tag: text-classification
+library_name: transformers
+datasets:
+  - imdb
 model-index:
-- name: tuning
-  results: []
+  - name: omaressamrme/tuning
+    results:
+      - task:
+          type: text-classification
+          name: Sentiment Analysis
+        dataset:
+          name: IMDb
+          type: imdb
+          split: test
+        metrics:
+          - type: accuracy
+            value: 0.31
+          - type: f1
+            value: 0.0
+widget:
+  - text: "I absolutely loved this movie!"
+  - text: "This was a terrible film. I want my time back."
 ---
 
-<!-- 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: 0.0016
-- eval_accuracy: 0.31
-- eval_f1: 0.0
-- eval_runtime: 27.04
-- eval_samples_per_second: 18.491
-- eval_steps_per_second: 0.592
-- step: 0
+# omaressamrme/tuning
 
-## Model description
+Fine-tuned DistilBERT for sentiment analysis on the IMDb dataset.
 
-More information needed
-
-## Intended uses & limitations
+## 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
 
-More information needed
+## Evaluation (test split)
+- Accuracy: 0.31
+- F1 (binary): 0.0
 
-## Training and evaluation data
+## Usage
+```python
+from transformers import pipeline
+clf = pipeline("text-classification", model="omaressamrme/tuning")
+print(clf("I absolutely loved this movie!"))
+```
 
-More information needed
+## Batch inference
+You can batch texts using the pipeline:
+```python
+texts = ["Great film!", "Worst plot ever."]
+preds = clf(texts)
+```
 
-## 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
+## Intended uses & limitations
+- Intended for educational/demo sentiment classification.
+- Trained on a subset of IMDb for speed; performance is lower than full training.
+- May reflect dataset biases; do not use for critical decisions.
 
-- Transformers 4.57.0
-- Pytorch 2.8.0+cpu
-- Datasets 4.2.0
-- Tokenizers 0.22.1
+## Reproducibility
+See the training script in the associated GitHub repo.