Instructions to use NLPclass/MT5base_en2fa_translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NLPclass/MT5base_en2fa_translation with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NLPclass/MT5base_en2fa_translation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("NLPclass/MT5base_en2fa_translation")
model = AutoModelForSeq2SeqLM.from_pretrained("NLPclass/MT5base_en2fa_translation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NLPclass/MT5base_en2fa_translation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NLPclass/MT5base_en2fa_translation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NLPclass/MT5base_en2fa_translation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NLPclass/MT5base_en2fa_translation

SGLang

How to use NLPclass/MT5base_en2fa_translation with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NLPclass/MT5base_en2fa_translation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NLPclass/MT5base_en2fa_translation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NLPclass/MT5base_en2fa_translation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NLPclass/MT5base_en2fa_translation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NLPclass/MT5base_en2fa_translation with Docker Model Runner:
```
docker model run hf.co/NLPclass/MT5base_en2fa_translation
```

MT5base_en2fa_translation / README.md

Nhoseinnia

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metadata

library_name: transformers
license: mit
datasets:
  - persiannlp/parsinlu_translation_en_fa
language:
  - fa
pipeline_tag: text2text-generation

Model Card for Model ID

using Mt5-Base for english to persian translation

Model Description

This model is designed to automatically translate English text to Farsi, which uses the pre-trained model of MT5, which is a multilingual seq2seq model.

Model type: MT5-base.
Language(s) (NLP): english to persian.

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 model train with 36.000 text en 2 fa from persiannlp/parsinlu_translation_en_fa dataSet

Training Hyperparameters

Number of Epochs: 2
Training Batch Size: 8
evaluation Batch Size: 8

Testing Data, Factors & Metrics

Testing Data

this model test with 4.000 text en 2 fa from persiannlp/parsinlu_translation_en_fa dataSet