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

Libraries

How to use thanmj/ABSA_HOS_MultiLA with Transformers:

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

pipe = pipeline("text-generation", model="thanmj/ABSA_HOS_MultiLA")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thanmj/ABSA_HOS_MultiLA")
model = AutoModelForSeq2SeqLM.from_pretrained("thanmj/ABSA_HOS_MultiLA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thanmj/ABSA_HOS_MultiLA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/thanmj/ABSA_HOS_MultiLA

SGLang

How to use thanmj/ABSA_HOS_MultiLA 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 "thanmj/ABSA_HOS_MultiLA" \
    --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": "thanmj/ABSA_HOS_MultiLA",
		"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 "thanmj/ABSA_HOS_MultiLA" \
        --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": "thanmj/ABSA_HOS_MultiLA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

Uses

Generation

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thanmj/ABSA_HOS_MultiLA")
model = AutoModelForSeq2SeqLM.from_pretrained("thanmj/ABSA_HOS_MultiLA")
input_text = 'very nice hotel. decent sized rooms. very helpful staff. Great location. the hotel would have been perfectly justified in making us pay. dealt with the situation admirably. Great service that makes them stand out from the crowd.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], , max_new_tokens = 200))

Inference

from transformers import pipeline

generator = pipeline("text2text-generation", model="thanmj/ABSA_HOS_MultiLA")
input_text = 'The room was spacious with a king size bed. decoration looks old that’s a shame. Staff does not speak well English. We had a lunch at the river coffee and had the worst meal since we arrived in Vietnam - I do not recommend. There a night market nearby. Breakfast really not tasty. Hotel has a nice location near the river however it is quite far from the center ( 2kms ) and you just want to see something else. you can find taxi anyway.'
generator(input_text, max_length = 200)

Downloads last month: 8

Evaluation results

Precision on hotel_orgen_vn2en_fbm2m1B
self-reported

0.878