Update README.md

README.md

@@ -1,199 +1,274 @@
 ---
+language:
+- bn
+- en
 library_name: transformers
-tags: []
+license: apache-2.0
+tags:
+- tokenizer
+- sentencepiece
+- bengali
+- banglish
+- multilingual
+- transformers
+- nlp
+- gpt
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Friday Tokenizer
 
+Friday Tokenizer is a custom multilingual tokenizer built completely from scratch for Bengali, English, and Banglish conversational AI systems.
 
+---
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+Friday Tokenizer is a SentencePiece-based subword tokenizer designed for lightweight GPT-style language models and conversational AI applications. It was developed as part of the Friday GPT project to support Bengali and multilingual NLP without relying on existing pre-trained tokenizers.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The tokenizer is optimized for conversational datasets, mixed Bengali-English text, and Banglish (Romanized Bengali) inputs.
 
-- **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:** Debashish Roy
+- **Funded by [optional]:** Self-funded
+- **Shared by [optional]:** Debashish Roy
+- **Model type:** SentencePiece Tokenizer
+- **Language(s) (NLP):** Bengali, English, Banglish
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** None (built from scratch)
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** https://huggingface.co/thedeba/friday-tokenizer
+- **Paper [optional]:** Not available
+- **Demo [optional]:** Not available
 
-- **Repository:** [More Information Needed]
-- **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
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This tokenizer is intended for:
 
-[More Information Needed]
+- GPT-style decoder-only language models
+- Conversational AI systems
+- Bengali NLP experiments
+- Banglish text generation
+- Lightweight multilingual language models
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The tokenizer can be integrated into:
 
-[More Information Needed]
+- Chatbots
+- Language generation systems
+- Translation systems
+- Bengali AI assistants
+- Custom transformer training pipelines
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This tokenizer is not optimized for:
 
-[More Information Needed]
+- Formal literary Bengali
+- Legal or medical NLP applications
+- High-precision linguistic analysis
+- Production-scale multilingual systems without further evaluation
+
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The tokenizer was trained primarily on conversational and subtitle-style datasets. As a result:
 
-[More Information Needed]
+- Informal language patterns may be overrepresented
+- Rare words may split aggressively
+- Banglish spelling inconsistencies may affect tokenization quality
+- Dataset biases from subtitle and internet conversations may exist
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should evaluate tokenizer performance before deploying it in sensitive or production environments. Additional fine-tuning or vocabulary expansion may improve performance for specialized domains.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+---
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to get started with the tokenizer.
+
+```python
+from transformers import AutoTokenizer
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(
+    "thedeba/friday-tokenizer",
+    use_fast=False
+)
+
+text = "আমি আজ বাইরে যাচ্ছি"
+
+tokens = tokenizer.tokenize(text)
+ids = tokenizer.encode(text)
+
+print(tokens)
+print(ids)
+
+decoded = tokenizer.decode(ids)
+print(decoded)
+```
+
+---
 
 ## 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. -->
+The tokenizer was trained using mixed multilingual conversational datasets including:
 
-[More Information Needed]
+- OpenSubtitles
+- Bengali conversational text
+- Bengali-English mixed text
+- Banglish datasets
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The tokenizer was trained from scratch using SentencePiece subword tokenization.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+- Unicode normalization
+- Text cleaning
+- Duplicate filtering
+- Mixed-language corpus preparation
 
 #### 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 -->
+- **Vocabulary Size:** 32000
+- **Training regime:** SentencePiece subword training
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- Lightweight tokenizer suitable for low-resource devices
+- Compact vocabulary size for efficient inference
 
-[More Information Needed]
+---
 
 ## 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]
+Internal conversational Bengali-English text samples were used for qualitative evaluation.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Evaluation focused on:
 
-[More Information Needed]
+- Bengali Unicode support
+- Mixed-language tokenization
+- Banglish handling
+- Conversational token quality
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Qualitative tokenization inspection and reconstruction accuracy were primarily used.
 
 ### Results
 
-[More Information Needed]
+The tokenizer successfully supports multilingual conversational tokenization with efficient subword segmentation.
 
 #### Summary
 
+Friday Tokenizer provides lightweight multilingual tokenization suitable for GPT-style language models and Bengali conversational AI applications.
 
+---
 
 ## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
+Basic qualitative inspection was performed to verify token splitting and text reconstruction quality.
 
-[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 emissions were not formally tracked during tokenizer training.
 
-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:** Consumer GPU / CPU
+- **Hours used:** Not recorded
+- **Cloud Provider:** Google Colab
+- **Compute Region:** Not specified
+- **Carbon Emitted:** Unknown
 
-- **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]
+- Architecture: SentencePiece tokenizer
+- Objective: Multilingual subword tokenization for conversational AI
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Training was performed using local and cloud-based environments.
 
 #### Hardware
 
-[More Information Needed]
+- Consumer-grade hardware
+- Google Colab environment
 
 #### Software
 
-[More Information Needed]
+- Python
+- SentencePiece
+- Hugging Face Transformers
+
+---
 
 ## 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
 
-**BibTeX:**
+```bibtex
+@misc{fridaytokenizer2026,
+  title={Friday Tokenizer},
+  author={Debashish Roy},
+  year={2026},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/thedeba/friday-tokenizer}}
+}
+```
 
-[More Information Needed]
+### APA
 
-**APA:**
+Roy, D. (2026). *Friday Tokenizer*. Hugging Face. https://huggingface.co/thedeba/friday-tokenizer
 
-[More Information Needed]
+---
 
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- **Banglish:** Bengali written using the Latin alphabet
+- **Subword Tokenization:** Splitting words into smaller meaningful units
+- **SentencePiece:** A language-independent tokenizer and text segmentation library
 
-[More Information Needed]
+---
 
 ## More Information [optional]
 
-[More Information Needed]
+Friday Tokenizer is part of the broader Friday GPT ecosystem focused on building multilingual lightweight AI systems from scratch.
+
+---
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Debashish Roy
+
+---
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or collaboration:
+
+- Hugging Face: https://huggingface.co/thedeba
+```