Instructions to use monsoon-nlp/ar-seq2seq-gender-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monsoon-nlp/ar-seq2seq-gender-decoder with Transformers:

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

pipe = pipeline("text-generation", model="monsoon-nlp/ar-seq2seq-gender-decoder")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/ar-seq2seq-gender-decoder")
model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/ar-seq2seq-gender-decoder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use monsoon-nlp/ar-seq2seq-gender-decoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monsoon-nlp/ar-seq2seq-gender-decoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/monsoon-nlp/ar-seq2seq-gender-decoder

SGLang

How to use monsoon-nlp/ar-seq2seq-gender-decoder 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 "monsoon-nlp/ar-seq2seq-gender-decoder" \
    --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": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"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 "monsoon-nlp/ar-seq2seq-gender-decoder" \
        --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": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monsoon-nlp/ar-seq2seq-gender-decoder with Docker Model Runner:
```
docker model run hf.co/monsoon-nlp/ar-seq2seq-gender-decoder
```

ar-seq2seq-gender-decoder / README.md

monsoon-nlp

sample code

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preview code

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	---
	language: ar
	---

	# ar-seq2seq-gender (decoder)

	This is a seq2seq model (decoder half) to "flip" gender in first-person Arabic sentences.
	The model can augment your existing Arabic data, or generate counterfactuals
	to test a model's decisions (would changing the gender of the subject or speaker change output?).

	Intended Examples:
	- 'أنا سعيد' <=> 'انا سعيدة'
	- 'ركض إلى المتجر' <=> 'ركضت إلى المتجر'

	People's names, gender pronouns, gendered words (father, mother), and many other values are currently unchanged by this model. Future versions may be trained on more data.

	## Sample Code

	```
	import torch
	from transformers import AutoTokenizer, EncoderDecoderModel

	model = EncoderDecoderModel.from_encoder_decoder_pretrained(
	"monsoon-nlp/ar-seq2seq-gender-encoder",
	"monsoon-nlp/ar-seq2seq-gender-decoder",
	min_length=40
	)
	tokenizer = AutoTokenizer.from_pretrained('monsoon-nlp/ar-seq2seq-gender-decoder') # same as MARBERT original

	input_ids = torch.tensor(tokenizer.encode("أنا سعيدة")).unsqueeze(0)
	generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
	tokenizer.decode(generated.tolist()[0][1 : len(input_ids[0]) - 1])
	> 'انا سعيد'
	```

	https://colab.research.google.com/drive/1S0kE_2WiV82JkqKik_sBW-0TUtzUVmrV?usp=sharing

	## Training

	I originally developed
	<a href="https://github.com/MonsoonNLP/el-la">a gender flip Python script</a>
	for Spanish sentences, using
	<a href="https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased">BETO</a>,
	and spaCy. More about this project: https://medium.com/ai-in-plain-english/gender-bias-in-spanish-bert-1f4d76780617

	The Arabic model encoder and decoder started with weights and vocabulary from
	<a href="https://github.com/UBC-NLP/marbert">MARBERT from UBC-NLP</a>,
	and was trained on the
	<a href="https://camel.abudhabi.nyu.edu/arabic-parallel-gender-corpus/">Arabic Parallel Gender Corpus</a>
	from NYU Abu Dhabi. The text is first-person sentences from OpenSubtitles, with parallel
	gender-reinflected sentences generated by Arabic speakers.

	Training notebook: https://colab.research.google.com/drive/1TuDfnV2gQ-WsDtHkF52jbn699bk6vJZV

	## Non-binary gender

	This model is useful to generate male and female text samples, but falls
	short of capturing gender diversity in the world and in the Arabic
	language. This subject is discussed in the bias statement of the
	<a href="https://www.aclweb.org/anthology/2020.gebnlp-1.12/">Gender Reinflection paper</a>.