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--- |
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language: en |
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license: cc-by-4.0 |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: weijiang2009/AlgmonQuestingAnsweringModel |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_v2 |
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type: squad_v2 |
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config: squad_v2 |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 79.9309 |
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name: Exact Match |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhNjg5YzNiZGQ1YTIyYTAwZGUwOWEzZTRiYzdjM2QzYjA3ZTUxNDM1NjE1MTUyMjE1MGY1YzEzMjRjYzVjYiIsInZlcnNpb24iOjF9.EH5JJo8EEFwU7osPz3s7qanw_tigeCFhCXjSfyN0Y1nWVnSfulSxIk_DbAEI5iE80V4EKLyp5-mYFodWvL2KDA |
|
- type: f1 |
|
value: 82.9501 |
|
name: F1 |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjk5ZDYwOGQyNjNkMWI0OTE4YzRmOTlkY2JjNjQ0YTZkNTMzMzNkYTA0MDFmNmI3NjA3NjNlMjhiMDQ2ZjJjNSIsInZlcnNpb24iOjF9.DDm0LNTkdLbGsue58bg1aH_s67KfbcmkvL-6ZiI2s8IoxhHJMSf29H_uV2YLyevwx900t-MwTVOW3qfFnMMEAQ |
|
- type: total |
|
value: 11869 |
|
name: total |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5_MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tX_dIsauHWtUpIHMPZCf5dlMWQKXZuAAA |
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--- |
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# algmon-base for QA |
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|
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This is the base model for QA [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. |
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## Overview |
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**Language model:** roberta-base |
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**Language:** English |
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**Downstream-task:** Extractive QA |
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**Training data:** SQuAD 2.0 |
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**Eval data:** SQuAD 2.0 |
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**Infrastructure**: 4x Tesla v100 |
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|
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## Hyperparameters |
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|
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``` |
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batch_size = 96 |
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n_epochs = 2 |
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base_LM_model = "roberta-base" |
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max_seq_len = 386 |
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learning_rate = 3e-5 |
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lr_schedule = LinearWarmup |
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warmup_proportion = 0.2 |
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doc_stride=128 |
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max_query_length=64 |
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``` |
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## Usage |
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### In Haystack |
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): |
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```python |
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reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") |
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# or |
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reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") |
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``` |
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For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) |
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### In Transformers |
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|
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "deepset/roberta-base-squad2" |
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# a) Get predictions |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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QA_input = { |
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'question': 'Why is model conversion important?', |
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' |
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} |
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res = nlp(QA_input) |
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# b) Load model & tokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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## Performance |
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Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). |
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|
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``` |
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"exact": 79.87029394424324, |
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"f1": 82.91251169582613, |
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|
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"total": 11873, |
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"HasAns_exact": 77.93522267206478, |
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"HasAns_f1": 84.02838248389763, |
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"HasAns_total": 5928, |
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"NoAns_exact": 81.79983179142137, |
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"NoAns_f1": 81.79983179142137, |
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"NoAns_total": 5945 |
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``` |
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