Update README.md

updated by handling data distro. and optimized training params.

README.md

@@ -1,199 +1,239 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- sentiment-analysis
+- distilbert
+- text-classification
+- nlp
+- imdb
+- binary-classification
+license: mit
+datasets:
+- stanfordnlp/imdb
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- distilbert/distilbert-base-uncased
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+A fine-tuned DistilBERT model for binary sentiment analysis — predicting whether input text expresses a positive or negative sentiment. Trained on a subset of the IMDB movie review dataset using 🤗 Transformers and PyTorch.
 
 ## 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.
+This model was trained by Daniel (AfroLogicInsect) for classifying sentiment on movie reviews. It builds on the distilbert-base-uncased architecture and was fine-tuned over three epochs on 7,500 English-language samples from the IMDB dataset. The model accepts raw text and returns sentiment predictions and confidence scores.
 
-- **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]
+- **Developed by:** Daniel 🇳🇬 (@AfroLogicInsect)
+- **Funded by:** [More Information Needed]
+- **Shared by:** [More Information Needed]
+- **Model type:** DistilBERT-based sequence classification
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** distilbert-base-uncased
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://huggingface.co/AfroLogicInsect/sentiment-analysis-model_v2
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
 ## 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 short texts, reviews, feedback forms, etc.
+- Embedding in web apps or chatbots to assess user mood or response tone
 
-<!-- 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]
+- Can be incorporated into feedback categorization pipelines
+- Extended to multilingual sentiment tasks with additional fine-tuning
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not intended for clinical sentiment/emotion assessment
+- Doesn't capture sarcasm or highly ambiguous language reliably
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- Biases may be inherited from the IMDB dataset (e.g. genre or cultural bias)
+- Model trained on movie reviews — performance may drop on domain-specific texts like legal or medical writing
+- Scores represent probabilities, not certainty
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Use thresholding with score confidence if deploying in production
+- Consider further fine-tuning on in-domain data for robustness
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```{python}
+from transformers import pipeline
+
+classifier = pipeline("sentiment-analysis", model="AfroLogicInsect/sentiment-analysis-model")
+result = classifier("Absolutely loved it!")
+print(result)
+```
 
-[More Information Needed]
 
 ## 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]
-
-### 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]
-
+- Subset of stanfordnlp/imdb
+- Balanced binary classes (positive and negative)
+- Sample size: ~15,000 training / 1,500 validation
 
 #### Training Hyperparameters
 
-- **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]
+##### Training arguments
+training_args = TrainingArguments(
+    output_dir = "./sentiment-model-v2",
+    num_train_epochs=3,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    learning_rate=2e-5,  # Explicit learning rate
+    warmup_steps=100,    # Reduced warmup
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=50,
+    eval_strategy="steps",
+    eval_steps=200,      # < 500: More frequent evaluation
+    save_strategy="steps",
+    save_steps=200, # match eval_steps
+    load_best_model_at_end=True,
+    metric_for_best_model="f1",
+    greater_is_better=True,
+    seed=42,             # Reproducibility
+    dataloader_drop_last=False,
+    #remove_unused_columns=False,
+)
+
+##### Create trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=val_dataset,
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+)
 
 ## 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]
+- Validation set from IMDB subset
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
 
-### Results
+Step	Training Loss	Validation Loss	Accuracy	F1	Precision	Recall
+200	0.391100	0.344377	0.850000	0.863554	0.791991	0.949333
+400	0.299000	0.304345	0.876000	0.865994	0.942006	0.801333
+600	0.301700	0.298436	0.881333	0.888331	0.838863	0.944000
+800	0.280700	0.260090	0.893333	0.897698	0.862408	0.936000
+1000	0.173100	0.288142	0.899333	0.897766	0.911967	0.884000
+1200	0.203700	0.263154	0.904667	0.905486	0.897772	0.913333
+1400	0.186100	0.275240	0.904000	0.901370	0.926761	0.877333
+1600	0.130400	0.291926	0.904667	0.903313	0.916324	0.890667
+1800	0.158900	0.304814	0.908000	0.908488	0.903694	0.913333
+2000	0.087900	0.332357	0.904000	0.905263	0.893506	0.917333
+2200	0.119300	0.339073	0.908667	0.910399	0.893453	0.928000
+2400	0.178100	0.366023	0.903333	0.905660	0.884371	0.928000
+2600	0.072100	0.372015	0.909333	0.908356	0.918256	0.898667
+2800	0.097700	0.368600	0.906667	0.908016	0.895078	0.921333
+
+Final evaluation results: {
+  'eval_loss': 0.3390733003616333, 
+  'eval_accuracy': 0.9086666666666666, 
+  'eval_f1': 0.9103989535644212, 
+  'eval_precision': 0.8934531450577664, 
+  'eval_recall': 0.928, 
+  'eval_runtime': 9.9181, 
+  'eval_samples_per_second': 151.239, 
+  'eval_steps_per_second': 9.478, 'epoch': 3.0
+}
+
+
+### Results [Sample]
+
+#### ============================================================
+#### TESTING FIXED MODEL
+#### ============================================================
+
+Testing fixed model...
+Text                                               Expected   Predicted  Confidence Match
+==========================================================================================
+I absolutely loved this movie! It was fantastic!   positive   positive   0.9959     ✓
+This movie was terrible and boring.                negative   negative   0.9969     ✓
+Amazing acting and great story!                    positive   positive   0.9959     ✓
+Worst film I've ever seen.                         negative   negative   0.9950     ✓
+Incredible cinematography and soundtrack.          positive   positive   0.9950     ✓
+Complete waste of time and money.                  negative   negative   0.9957     ✓
+The movie was okay, nothing special.               neutral    negative   0.9915     N/A
+I enjoyed most of it.                              positive   positive   0.9912     ✓
+Pretty disappointing overall.                      negative   negative   0.9936     ✓
+Masterpiece of cinema!                             positive   positive   0.9939     ✓
+
+Overall Accuracy: 100.0% (9/9)
+
+## 🧪 Live Demo
+
+Try it out below!
+
+👉 [Launch Sentiment Analyzer](https://huggingface.co/spaces/AfroLogicInsect/sentiment-analysis-model-gradio)
 
-[More Information Needed]
 
 #### Summary
 
+The model performs well on balanced sentiment data and generalizes across a variety of movie review tones. Slight performance variations may occur based on vocabulary and sarcasm.
 
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon footprint estimated using [ML Impact Calculator](https://mlco2.github.io/impact#compute)
 
-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]
+Hardware Type: GPU (single NVIDIA T4)
+Hours used: ~2.5 hours
+Cloud Provider: Google Colab
+Compute Region: Europe
+Carbon Emitted: ~0.3 kg CO₂eq
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+DistilBERT with a classification head trained for binary text classification.
 
 ### Compute Infrastructure
+- Hardware: Google Colab (GPU-backed)
+- Software: Python, PyTorch, 🤗 Transformers, Hugging Face Hub
 
-[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. -->
+## Citation 
 
 **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]
+[@misc{afrologicinsect2025sentiment,
+  title = {AfroLogicInsect Sentiment Analysis Model},
+  author = {Akan Daniel},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/AfroLogicInsect/sentiment-analysis-model_v2}},
+}]
 
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+- Name: Daniel (@AfroLogicInsect)
+- Location: Lagos, Nigeria
+- Contact: GitHub / Hugging Face / email ([email protected])