deepset/covid_qa_deepset
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How to use hung200504/bert-covidqa-5 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="hung200504/bert-covidqa-5") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-covidqa-5")
model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-covidqa-5", device_map="auto")This model is a fine-tuned version of timpal0l/mdeberta-v3-base-squad2 on the covid_qa_deepset dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5956 | 0.04 | 5 | 0.4016 |
| 0.3741 | 0.09 | 10 | 0.3879 |
| 0.3405 | 0.13 | 15 | 0.4240 |
| 0.4372 | 0.18 | 20 | 0.4102 |
| 0.2592 | 0.22 | 25 | 0.4534 |
| 0.3534 | 0.26 | 30 | 0.4571 |
| 0.4268 | 0.31 | 35 | 0.4107 |
| 0.2663 | 0.35 | 40 | 0.4166 |
| 0.143 | 0.39 | 45 | 0.4345 |
| 0.2494 | 0.44 | 50 | 0.5575 |
| 0.8953 | 0.48 | 55 | 0.6172 |
| 0.5504 | 0.53 | 60 | 0.4879 |
| 0.6411 | 0.57 | 65 | 0.3718 |
| 0.5454 | 0.61 | 70 | 0.3929 |
| 0.4441 | 0.66 | 75 | 0.3641 |
| 0.2922 | 0.7 | 80 | 0.3638 |
| 0.491 | 0.75 | 85 | 0.3785 |
| 0.4362 | 0.79 | 90 | 0.3938 |
| 0.1633 | 0.83 | 95 | 0.4162 |
| 0.6762 | 0.88 | 100 | 0.4321 |
| 0.3111 | 0.92 | 105 | 0.4241 |
| 0.3453 | 0.96 | 110 | 0.4190 |
Base model
microsoft/mdeberta-v3-base