Mikile
/

Bertha-translation

@@ -1,146 +1,71 @@
-# 🧠 English -> Bertha Translation (Bertha-Translation)
-**Bertha-Translation** is a bilingual **Seq2Seq neural machine translation model** that translates text from **Bertha** to **English**.
-It’s built using the **official PyTorch Seq2Seq tutorial architecture (Encoder–Decoder GRU)** and trained on a **cleaned custom dataset (`lexicon.csv`)**.
 ---
-## 🏗️ Model Details
-- **Author:** Mikiyas Zenebe
-- **Model type:** Seq2Seq (Encoder + Decoder)
 - **Framework:** PyTorch
-- **Source language:** Bertha
-- **Target language:** English
 - **License:** Apache 2.0
-- **Trained with:** official PyTorch translation tutorial structure
-- **Uploaded on:** Hugging Face Hub (`Mikile/Bertha-translation`)
 ---
-## 🚀 Usage Example
 ```python
 import torch
-from model import EncoderRNN, DecoderRNN  # your model classes
 from huggingface_hub import hf_hub_download
 # Download trained models
 encoder_path = hf_hub_download("Mikile/Bertha-translation", "encoder.pth")
 decoder_path = hf_hub_download("Mikile/Bertha-translation", "decoder.pth")
-<!-- 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]

+---
+language: ["en", "ber"]
+license: "apache-2.0"
+tags:
+  - translation
+  - seq2seq
+  - machine-translation
+  - pytorch
+widget:
+  - text: "Hello my friend"
+  - text: "How are you?"
+---
+# Bertha ↔ English Translator
+**Bertha-Translation** is a bilingual Seq2Seq neural machine translation model that translates text **between English and Bertha**.
+It uses an **Encoder–Decoder GRU architecture with attention**.
+---
+## Features
+- Translate **English → Bertha**
+- Translate **Bertha → English**
+- Bidirectional Seq2Seq model with attention
+- Built with PyTorch following the official Seq2Seq tutorial
+- Lightweight and suitable for low-latency translation
 ---
+## Model Details
+- **Author:** Mikiyas Zenebe
+- **Model type:** Seq2Seq (Encoder + Decoder with Attention)
 - **Framework:** PyTorch
+- **Source / Target languages:** English, Bertha
 - **License:** Apache 2.0
 ---
+## Usage Example
 ```python
 import torch
 from huggingface_hub import hf_hub_download
+from model import EncoderRNN, DecoderRNN, tensorFromSentence
 # Download trained models
 encoder_path = hf_hub_download("Mikile/Bertha-translation", "encoder.pth")
 decoder_path = hf_hub_download("Mikile/Bertha-translation", "decoder.pth")
+# Load your encoder and decoder
+encoder = EncoderRNN(input_size, hidden_size)
+decoder = DecoderRNN(hidden_size, output_size)
+encoder.load_state_dict(torch.load(encoder_path))
+decoder.load_state_dict(torch.load(decoder_path))
+# Translate a sentence
+sentence = "hello my friend"
+input_tensor = tensorFromSentence(input_lang, sentence)
+encoder_outputs, encoder_hidden = encoder(input_tensor)
+decoder_outputs, _, _ = decoder(encoder_outputs, encoder_hidden)
+@misc{bertha_translation,
+  author = {Mikiyas Zenebe},
+  title = {Bertha ↔ English Translation Model},
+  year = {2025},
+  howpublished = {Hugging Face Model Hub},
+  url = {https://huggingface.co/Mikile/Bertha-translation}
+}