End of training

README.md

@@ -1,73 +1,58 @@
 ---
-language:
-- en
+library_name: transformers
 license: apache-2.0
+base_model: distilbert-base-uncased
 tags:
-- text-classification
-- sentiment-analysis
-- imdb
-- distilbert
-pipeline_tag: text-classification
-library_name: transformers
-datasets:
-- imdb
-widget:
-- text: I absolutely loved this movie!
-- text: This was a terrible film. I want my time back.
+- generated_from_trainer
 model-index:
-- 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
+- name: tuning
+  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. -->
 
-# omaressamrme/tuning
+# tuning
 
-Fine-tuned DistilBERT for sentiment analysis on the IMDb dataset.
+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: 27.5721
+- eval_samples_per_second: 18.134
+- eval_steps_per_second: 0.58
+- step: 0
 
-## 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
+## Model description
 
-## Evaluation (test split)
-- Accuracy: 0.31
-- F1 (binary): 0.0
+More information needed
 
-## Usage
-```python
-from transformers import pipeline
-clf = pipeline("text-classification", model="omaressamrme/tuning")
-print(clf("I absolutely loved this movie!"))
-```
+## Intended uses & limitations
 
-## Batch inference
-You can batch texts using the pipeline:
-```python
-texts = ["Great film!", "Worst plot ever."]
-preds = clf(texts)
-```
+More information needed
 
-## 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.
+## 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
 
-## Reproducibility
-See the training script in the associated GitHub repo.
+- Transformers 4.57.0
+- Pytorch 2.8.0+cpu
+- Datasets 4.2.0
+- Tokenizers 0.22.1

SPACE_README.md

@@ -3,7 +3,7 @@
 title: IMDb Sentiment Analysis
 emoji: ❤️
 colorFrom: purple
-colorTo: orange
+colorTo: pink
 sdk: gradio
 sdk_version: 4.40.0
 app_file: app.py

runtime.txt

@@ -0,0 +1 @@
+python-3.10

training_args.bin

@@ -1,3 +1,3 @@
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