abdulhade
/

fine-tuned-MarianMTKurdish

@@ -1,199 +1,100 @@
----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## 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]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **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. -->
-[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]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### 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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[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]
-#### 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]
-## 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]
-#### Metrics
-<!-- 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]
-## 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 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]

+# Creating a README.md file with the Hugging Face style content as per the user's request.
+readme_content = """
+# Kurdish-English Machine Translation with Hugging Face Transformers
+This repository focuses on fine-tuning a Kurdish-English machine translation model using Hugging Face's `transformers` library with MarianMT. The model is trained on a custom parallel corpus with a detailed pipeline that includes data preprocessing, bidirectional training, evaluation, and inference.
+## Table of Contents
+- [Introduction](#introduction)
+- [Requirements](#requirements)
+- [Setup](#setup)
+- [Pipeline Overview](#pipeline-overview)
+  - [Data Preparation](#data-preparation)
+  - [Training SentencePiece Tokenizer](#training-sentencepiece-tokenizer)
+  - [Model and Tokenizer Setup](#model-and-tokenizer-setup)
+  - [Tokenization and Dataset Preparation](#tokenization-and-dataset-preparation)
+  - [Training Configuration](#training-configuration)
+  - [Evaluation and Metrics](#evaluation-and-metrics)
+  - [Inference](#inference)
+- [Results](#results)
+- [License](#license)
+## Introduction
+This project fine-tunes a MarianMT model for Kurdish-English translation on a custom parallel corpus. Training is configured for bidirectional translation, enabling model use in both language directions.
+## Requirements
+- Python 3.8+
+- Hugging Face Transformers
+- Datasets library
+- SentencePiece
+- PyTorch 1.9+
+- CUDA (for GPU support)
+## Setup
+1. Clone the repository and install dependencies.
+2. Ensure GPU availability.
+3. Prepare your Kurdish-English corpus in CSV format.
+## Pipeline Overview
+### Data Preparation
+1. **Corpus**: A Kurdish-English parallel corpus in CSV format with columns `Source` (Kurdish) and `Target` (English).
+2. **Path Definition**: Specify the corpus path in the configuration.
+### Training SentencePiece Tokenizer
+- **Vocabulary Size**: 32,000
+- **Source Data**: The tokenizer is trained on both the primary Kurdish corpus and the English dataset to create shared subword tokens.
+### Model and Tokenizer Setup
+- **Model**: `Helsinki-NLP/opus-mt-en-mul` pre-trained MarianMT model.
+- **Tokenizer**: MarianMT tokenizer aligned with the model, with source and target languages set dynamically.
+### Tokenization and Dataset Preparation
+- **Train-Validation Split**: 90% train, 10% validation split.
+- **Maximum Sequence Length**: 128 tokens for both source and target sequences.
+- **Bidirectional Tokenization**: Prepare tokenized sequences for both Kurdish-English and English-Kurdish translation.
+### Training Configuration
+- **Learning Rate**: 2e-5
+- **Batch Size**: 4 (per device, for both training and evaluation)
+- **Weight Decay**: 0.01
+- **Evaluation Strategy**: Per epoch
+- **Epochs**: 3
+- **Logging**: Logs saved every 100 steps, with TensorBoard logging enabled
+- **Output Directory**: `./results`
+- **Device**: GPU 1 explicitly set
+### Evaluation and Metrics
+The following metrics are computed on the validation dataset:
+- **BLEU**: Measures translation quality based on precision and recall of n-grams.
+- **METEOR**: Considers synonymy and stem matches.
+- **BERTScore**: Evaluates semantic similarity with BERT embeddings.
+### Inference
+Inference includes bidirectional translation capabilities:
+- **Source to Target**: English to Kurdish translation.
+- **Target to Source**: Kurdish to English translation.
+## Results
+The fine-tuned model and tokenizer are saved to `./fine-tuned-marianmt`, including evaluation metrics across BLEU, METEOR, and BERTScore.
+"""
+# Write the content to README.md
+file_path = "/mnt/data/README.md"
+with open(file_path, "w") as readme_file:
+    readme_file.write(readme_content)
+file_path