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my-test-model

Text Classification
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distilbert
Generated from Trainer
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my-test-model
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---
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
---



# my-test-model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on imdb dataset.


## 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