onnx-community/all-MiniLM-L12-v2-qa-all-ONNX

all-MiniLM-L12-v2-qa-all (ONNX)

This is an ONNX version of LLukas22/all-MiniLM-L12-v2-qa-all. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for question-answering: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.QuestionAnsweringPipeline

all-MiniLM-L12-v2-qa-all

This model is an extractive qa model. It's a fine-tuned version of all-MiniLM-L12-v2 on the following datasets: squad_v2, LLukas22/nq-simplified, newsqa, LLukas22/NLQuAD, deepset/germanquad.

Usage

You can use the model like this:

from transformers import pipeline

#Make predictions
model_name = "LLukas22/all-MiniLM-L12-v2-qa-all"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

QA_input = {
    "question": "What's my name?",
    "context": "My name is Clara and I live in Berkeley."
}

result = nlp(QA_input)
print(result)

Alternatively you can load the model and tokenizer on their own:

from transformers import AutoModelForQuestionAnswering, AutoTokenizer

#Make predictions
model_name = "LLukas22/all-MiniLM-L12-v2-qa-all"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2E-05
• per device batch size: 60
• effective batch size: 180
• seed: 42
• optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
• weight decay: 1E-02
• D-Adaptation: False
• Warmup: True
• number of epochs: 15
• mixed_precision_training: bf16

Training results

Evaluation results

Framework versions

• Transformers: 4.25.1
• PyTorch: 2.0.0.dev20230210+cu118
• PyTorch Lightning: 1.8.6
• Datasets: 2.7.1
• Tokenizers: 0.13.1
• Sentence Transformers: 2.2.2

Additional Information

This model was trained as part of my Master's Thesis 'Evaluation of transformer based language models for use in service information systems'. The source code is available on Github.