Update README.md

README.md

@@ -1,199 +1,150 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- sentiment-analysis
+- imdb
+- text-classification
+- distilbert
+license: apache-2.0
+datasets:
+- stanfordnlp/imdb
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- distilbert/distilbert-base-uncased
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for DistilBERT Fine-Tuned on IMDB Sentiment Analysis
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+This model is a fine-tuned version of `distilbert-base-uncased` on the **IMDB movie reviews dataset** for **binary sentiment classification** (positive vs. negative). The model has been trained to classify movie reviews into either **positive (1)** or **negative (0)** sentiments.
 
-### Model Sources [optional]
+- **Developed by:** Nikke Salonen
+- **Finetuned from model:** `distilbert-base-uncased`
+- **Language(s):** English
+- **License:** Apache 2.0
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources
+- **Repository:** https://huggingface.co/NikkeS/imdb-distilbert/
+- **Dataset:** [IMDB Dataset](https://ai.stanford.edu/~amaas/data/sentiment/)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
+- Sentiment analysis of **English text reviews**.
+- Can be used for **opinion mining** on movie reviews and similar datasets.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+### Downstream Use
+- Can be **fine-tuned further** for sentiment classification in other domains (e.g., product reviews, social media sentiment analysis).
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not suitable for **languages other than English**.
+- Not recommended for **high-stakes decision-making** without human oversight.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model is **trained on IMDB reviews**, so it may **not generalize well** to other types of sentiment analysis tasks.
+- May exhibit **biases present in the training data**.
+- Sentiment classification **depends heavily on context**, and the model may misinterpret sarcasm or complex sentences.
 
 ### Recommendations
+- Users should **evaluate the model** on their specific datasets before deploying in production.
+- If biases are detected, consider **fine-tuning on a more diverse dataset**.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Use the Model
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-## How to Get Started with the Model
+# Load the fine-tuned model from Hugging Face Hub
+model = AutoModelForSequenceClassification.from_pretrained("your-hf-username/imdb-distilbert")
+tokenizer = AutoTokenizer.from_pretrained("your-hf-username/imdb-distilbert")
 
-Use the code below to get started with the model.
+def predict_sentiment(review):
+    inputs = tokenizer(review, return_tensors="pt", truncation=True, padding=True, max_length=256)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    prediction = torch.argmax(logits, dim=1).item()
+    return "Positive" if prediction == 1 else "Negative"
 
-[More Information Needed]
+# Example Usage
+print(predict_sentiment("This movie was absolutely fantastic!"))
+print(predict_sentiment("The acting was terrible, and the story made no sense."))
+```
 
 ## Training Details
 
 ### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+- The model was fine-tuned on the **IMDB dataset** (50,000 labeled movie reviews).
+- The dataset is **balanced** (25,000 positive and 25,000 negative reviews).
 
 ### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+#### Preprocessing
+- Tokenized using `distilbert-base-uncased` tokenizer.
+- Applied **dynamic padding, truncation, and a max sequence length of 256**.
 
 #### Training Hyperparameters
+- **Learning rate:** `5e-5`
+- **Batch size:** `16`
+- **Epochs:** `2`
+- **Optimizer:** AdamW
+- **Loss Function:** Cross-Entropy Loss
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+#### Compute Infrastructure
+- **Hardware:** Google Colab T4 GPU
+- **Precision:** Mixed precision (`fp16=True` for efficiency)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
-
 #### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- The model was evaluated on a **10,000-sample test set** from the IMDB dataset.
 
 #### Metrics
+- **Accuracy:** 92,4%
+- **Precision, Recall, F1-score:**
+  - **Precision:** 92,4%
+  - **Recall:** 92.3%
+  - **F1-score:** 92.3%
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+## Model Examination
+- The model performs well on **general sentiment classification** but may struggle with **sarcasm, irony, or very short reviews**.
 
 ## Environmental Impact
+- **Hardware Type:** Google Colab T4 GPU
+- **Training Time:** ~1 hour
+- **CO2 Emission Estimate:** [Use ML Impact Calculator](https://mlco2.github.io/impact#compute)
+
+## Citation
+If you use this model, please cite:
+```bibtex
+@article{salonen2025imdb-distilbert,
+  title={Fine-tuned DistilBERT for Sentiment Analysis on IMDB Reviews},
+  author={Nikke Salonen},
+  year={2025}
+}
+```
+
+## More Information
+- **Hugging Face Model Page:** https://huggingface.co/NikkeS/imdb-distilbert/.
+- **Dataset:** [IMDB Dataset](https://ai.stanford.edu/~amaas/data/sentiment/)
+
+## Model Card Authors
+- [Nikke Salonen]
+
+## Contact
+For questions or issues, contact **[email protected]**.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+---
 
-[More Information Needed]
+This model card provides all necessary details, including **training info, evaluation results, and usage instructions**. Let me know if you'd like any modifications before uploading to **Hugging Face Hub**!