Update README.md

README.md

@@ -1,65 +1,134 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: distilbert-base-uncased
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-- f1
-model-index:
-- name: my-test-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. -->
-
-# my-test-model
-
-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:
-- Loss: 0.3448
-- Accuracy: 0.9130
-- F1: 0.9130
-
-## 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: 64
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.2497        | 1.0   | 1563 | 0.2486          | 0.9026   | 0.9024 |
-| 0.1496        | 2.0   | 3126 | 0.2896          | 0.9135   | 0.9135 |
-| 0.1222        | 3.0   | 4689 | 0.3448          | 0.9130   | 0.9130 |
-
-
-### Framework versions
-
-- Transformers 4.52.3
-- Pytorch 2.7.0+cu128
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: my-test-model
+  results: []
+datasets:
+- stanfordnlp/imdb
+---
+
+<!-- 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. -->
+
+# my-test-model
+
+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:
+- Loss: 0.3448
+- Accuracy: 0.9130
+- F1: 0.9130
+
+## Model description
+
+This model is a fine-tuned version of DistilBERT-base-uncased for binary sentiment analysis on movie reviews. Key specifications:
+
+Task: Sentiment classification (positive/negative)
+
+Base Architecture: 6-layer distilled Transformer model
+
+Parameters: ~66 million (standard DistilBERT configuration)
+
+Output Labels:
+
+0 → "NEGATIVE"
+
+1 → "POSITIVE"
+
+## Intended uses & limitations
+
+Acceptable Use Cases ✅
+Sentiment analysis of English movie reviews
+
+Educational/research purposes for text classification
+
+Baseline model for entertainment industry applications
+
+Integration in sentiment analysis pipelines
+
+Limitations ⚠️
+Language Restriction: Only supports English text
+
+Domain Specificity: Optimized for movie reviews - performance degrades on other text types
+
+Bias Risks: May reflect demographic/cultural biases in training data
+
+Length Constraint: Maximum input length of 256 tokens (longer texts are truncated)
+
+Not Suitable For:
+
+Multilingual text analysis
+
+Sarcasm/irony detection
+
+Fine-grained sentiment analysis (e.g., detecting anger, excitement)
+
+## Training and evaluation data
+
+Training Data
+Dataset: IMDB Movie Reviews
+
+Size: 25,000 labeled examples
+
+Class Distribution:
+
+Positive: 12,500 (50%)
+
+Negative: 12,500 (50%)
+
+Preprocessing:
+
+Lowercasing
+
+DistilBERT tokenization (WordPiece)
+
+Dynamic padding
+
+Evaluation Data
+Test Set: Official IMDB test split (25,000 examples)
+
+## Training procedure
+
+TrainingArguments(
+  num_train_epochs=3,
+  per_device_train_batch_size=16,
+  per_device_eval_batch_size=64,
+  learning_rate=2e-5,
+  weight_decay=0.01,
+  evaluation_strategy="epoch",
+  save_strategy="epoch",
+  metric_for_best_model="accuracy"
+)
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.2497        | 1.0   | 1563 | 0.2486          | 0.9026   | 0.9024 |
+| 0.1496        | 2.0   | 3126 | 0.2896          | 0.9135   | 0.9135 |
+| 0.1222        | 3.0   | 4689 | 0.3448          | 0.9130   | 0.9130 |
+
+
+### Framework versions
+
+- Transformers 4.52.3
+- Pytorch 2.7.0+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.1