mansoorhamidzadeh
/

mt5_en_fa_translation

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: mit
+language:
+- fa
+tags:
+- persian
+- mt5-small
+- mt5
+- persian translation
+- seq2seq
+- farsi
 ---
+# Model Card: English to Persian Translation using MT5-Small
 ## Model Details
+**Model Description:**
+This model is designed to translate text from English to Persian (Farsi) using the MT5-Small architecture. MT5 is a multilingual variant of the T5 model, pretrained on a diverse set of languages.
+**Intended Use:**
+The model is intended for use in applications where automatic translation from English to Persian is required. It can be used for translating documents, web pages, or any other text-based content.
+**Model Architecture:**
+- **Model Type:** MT5-Small
+- **Language Pair:** English (en) to Persian (fa)
+## Training Data
+**Dataset:**
+The model was trained on a dataset consisting of 100,000 parallel sentences of English and Persian text. The data includes various sources to cover a wide range of topics and ensure diversity.
+**Data Preprocessing:**
+- Text normalization was performed to ensure consistency.
+- Tokenization was done using the SentencePiece tokenizer.
+## Training Procedure
+**Training Configuration:**
+- **Number of Epochs:** 4
+- **Batch Size:** 8
+- **Learning Rate:** 5e-5
+- **Optimizer:** AdamW
+**Hardware:**
+- **Training Environment:** NVIDIA P100 GPU
+- **Training Time:** Approximately 4 hours
+## How To Use
+```python
+import torch
+from transformers import pipeline, MT5ForConditionalGeneration, MT5Tokenizer, Text2TextGenerationPipeline
+# Function to translate using the pipeline
+def translate_with_pipeline(text):
+    translator = Text2TextGenerationPipeline(model='NLPclass/mt5_en_fa_translation',tokenizer='NLPclass/mt5_en_fa_translation')
+    return translator(text,, max_length=128,num_beams=4)[0]['generated_text']
+# Example usage
+text = "Hello, how are you?"
+# Using pipeline
+print("Pipeline Translation:", translate_with_pipeline(text))
+```
+## Ethical Considerations
+- The model's translations are only as good as the data it was trained on, and biases present in the training data may propagate through the model's outputs.
+- Users should be cautious when using the model for critical tasks, as automatic translations can sometimes be inaccurate or misleading.
+## Citation
+If you use this model in your research or applications, please cite it as follows:
+```bibtex
+@misc{your_name_2024_mt5_en_fa,
+  author = {mansoorhamidzadeh},
+  title = {English to Persian Translation using MT5-Small},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/mansoorhamidzadeh/mt5_en_fa_translation}},
+}