Instructions to use ilsilfverskiold/bart-keyword-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ilsilfverskiold/bart-keyword-extractor with Transformers:

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

pipe = pipeline("text-generation", model="ilsilfverskiold/bart-keyword-extractor")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ilsilfverskiold/bart-keyword-extractor")
model = AutoModelForSeq2SeqLM.from_pretrained("ilsilfverskiold/bart-keyword-extractor")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ilsilfverskiold/bart-keyword-extractor with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ilsilfverskiold/bart-keyword-extractor

SGLang

How to use ilsilfverskiold/bart-keyword-extractor 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 "ilsilfverskiold/bart-keyword-extractor" \
    --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": "ilsilfverskiold/bart-keyword-extractor",
		"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 "ilsilfverskiold/bart-keyword-extractor" \
        --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": "ilsilfverskiold/bart-keyword-extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ilsilfverskiold/bart-keyword-extractor with Docker Model Runner:
```
docker model run hf.co/ilsilfverskiold/bart-keyword-extractor
```

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

Model description

This model is a fine-tuned version of facebook/bart-large on a dataset in the hub called sunhaozhepy/ag_news_keywords_embeddings to extract main keywords from text. It achieves the following results on the evaluation set:

Loss: 0.6179

Intended use

from transformers import pipeline

pipe = pipeline('summarization', model='bart_keywords_model')
print(pipe("Aria Opera GPT version - All the browsers come with their own version of AI. So I gave it a try and ask it with LLM it was using. First if all it didn't understand the question. Then I explained and asked which version. I got the usual answer about a language model that is not aware of it's own model I find that curious, but also not transparent. My laptop, software all state their versions and critical information. But something that can easily fool a lot of people doesn't. What I also wonder if the general public will be stuck to ChatGPT 3.5 for ever while better models are behind expensive paywalls."))

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.7701	0.57	500	0.7390
0.5804	1.14	1000	0.7056
0.5395	1.71	1500	0.6811
0.4036	2.28	2000	0.6504
0.3763	2.85	2500	0.6179

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 11

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for ilsilfverskiold/bart-keyword-extractor

Base model

facebook/bart-large

Finetuned

(197)

this model

ilsilfverskiold
/

bart-keyword-extractor