Instructions to use research-backup/bart-base-squad-qg-default with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use research-backup/bart-base-squad-qg-default with Transformers:

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

pipe = pipeline("text-generation", model="research-backup/bart-base-squad-qg-default")

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

tokenizer = AutoTokenizer.from_pretrained("research-backup/bart-base-squad-qg-default")
model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/bart-base-squad-qg-default", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use research-backup/bart-base-squad-qg-default with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/research-backup/bart-base-squad-qg-default

SGLang

How to use research-backup/bart-base-squad-qg-default 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 "research-backup/bart-base-squad-qg-default" \
    --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": "research-backup/bart-base-squad-qg-default",
		"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 "research-backup/bart-base-squad-qg-default" \
        --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": "research-backup/bart-base-squad-qg-default",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use research-backup/bart-base-squad-qg-default with Docker Model Runner:
```
docker model run hf.co/research-backup/bart-base-squad-qg-default
```

asahi417 commited on Oct 6, 2022

Commit

217337a

1 Parent(s): feae9ba

model update

Browse files

Files changed (1) hide show

README.md +5 -6

README.md CHANGED Viewed

@@ -14,11 +14,11 @@ pipeline_tag: text2text-generation
 tags:
 - question generation
 widget:
-- text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
   example_title: "Question Generation Example 1"
-- text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
   example_title: "Question Generation Example 2"
-- text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records <hl> ."
   example_title: "Question Generation Example 3"
 model-index:
 - name: lmqg/bart-base-squad-default
@@ -70,8 +70,7 @@ model_path = 'lmqg/bart-base-squad-default'
 pipe = pipeline("text2text-generation", model_path)
 # Question Generation
-input_text = 'generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.'
-question = pipe(input_text)
 ```
 ## Evaluation Metrics
@@ -81,7 +80,7 @@ question = pipe(input_text)
 | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
 |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
-| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.24400683975723286 | 0.5248717762722197 | 0.25985179074924764 | 0.9086545639846674 | 0.644778282518877 | [link](https://huggingface.co/lmqg/bart-base-squad-default/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) |

 tags:
 - question generation
 widget:
+- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
   example_title: "Question Generation Example 1"
+- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
   example_title: "Question Generation Example 2"
+- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records <hl> ."
   example_title: "Question Generation Example 3"
 model-index:
 - name: lmqg/bart-base-squad-default
 pipe = pipeline("text2text-generation", model_path)
 # Question Generation
+question = pipe('<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
 ```
 ## Evaluation Metrics
 | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
 |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
+| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.244 | 0.525 | 0.26 | 0.909 | 0.645 | [link](https://huggingface.co/lmqg/bart-base-squad-default/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) |