Instructions to use kevinscaria/joint_tk-instruct-base-def-pos-laptops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kevinscaria/joint_tk-instruct-base-def-pos-laptops with Transformers:

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

pipe = pipeline("text-generation", model="kevinscaria/joint_tk-instruct-base-def-pos-laptops")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("kevinscaria/joint_tk-instruct-base-def-pos-laptops")
model = AutoModelForSeq2SeqLM.from_pretrained("kevinscaria/joint_tk-instruct-base-def-pos-laptops")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kevinscaria/joint_tk-instruct-base-def-pos-laptops with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kevinscaria/joint_tk-instruct-base-def-pos-laptops

SGLang

How to use kevinscaria/joint_tk-instruct-base-def-pos-laptops 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 "kevinscaria/joint_tk-instruct-base-def-pos-laptops" \
    --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": "kevinscaria/joint_tk-instruct-base-def-pos-laptops",
		"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 "kevinscaria/joint_tk-instruct-base-def-pos-laptops" \
        --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": "kevinscaria/joint_tk-instruct-base-def-pos-laptops",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kevinscaria/joint_tk-instruct-base-def-pos-laptops with Docker Model Runner:
```
docker model run hf.co/kevinscaria/joint_tk-instruct-base-def-pos-laptops
```

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

joint_tk-instruct-base-def-pos-laptops

This model is finetuned for the Joint Task. The finetuning was carried out by adding prompts of the form:

definition + 2 positive examples

The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from the laptops domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here.

For the Joint Task, this model is the current SOTA.

Training data

InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels.

BibTeX entry and citation info

If you use this model in your work, please cite the following paper:

@inproceedings{Scaria2023InstructABSAIL,
  title={InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis},
  author={Kevin Scaria and Himanshu Gupta and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral},
  year={2023}
}

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Dataset used to train kevinscaria/joint_tk-instruct-base-def-pos-laptops

Paper for kevinscaria/joint_tk-instruct-base-def-pos-laptops

InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

Paper • 2302.08624 • Published Feb 16, 2023 • 3