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--- |
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license: mit |
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base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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tags: |
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- generated_from_trainer |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: bert-squadv2-biomed |
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results: |
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- task: |
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type: question-answering |
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dataset: |
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type: reading-comprehension |
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name: SQuADv2 |
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metrics: |
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- name: accuracy |
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type: accuracy |
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value: 0.88 |
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verified: false |
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language: |
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- en |
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pipeline_tag: question-answering |
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--- |
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# bert-squadv2-biomed |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the SQuADv2 dataset. It has been fine-tuned for question-answering tasks specifically related to biomedical texts, leveraging the SQuAD v2 dataset to enhance its ability to manage both answerable and unanswerable questions. |
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## Model Description |
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The base model, **PubMedBERT**, was originally pre-trained on biomedical abstracts and full-text articles from PubMed. This fine-tuned version adapts PubMedBERT for biomedical question-answering by training it with **SQuADv2**, a dataset that includes over 100,000 questions with answerable and unanswerable queries. |
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- **Use Cases**: This model is particularly useful in applications where quick and accurate question-answering from biomedical literature is needed. It is designed to provide answers to specific questions, as well as to detect when no relevant answer exists. |
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## Training and Evaluation Data |
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- **Dataset**: The model was fine-tuned on the **SQuADv2** dataset, which consists of reading comprehension tasks where some questions have no answer in the provided context. |
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- **Training Environment**: The model was trained in a Colab environment. A link to the training notebook can be found here: [Training Notebook](https://colab.research.google.com/drive/11je7-YnFQ-oISxC_7KS4QTfs3fgWOseU?usp=sharing). |
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## Training Procedure |
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### Hyperparameters |
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The following hyperparameters were used during training: |
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- `learning_rate`: 3e-05 |
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- `train_batch_size`: 16 |
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- `eval_batch_size`: 16 |
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- `seed`: 42 |
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- `optimizer`: Adam (betas=(0.9, 0.999), epsilon=1e-08) |
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- `lr_scheduler_type`: linear |
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- `num_epochs`: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 5.9623 | 0.02 | 5 | 5.8084 | |
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| 5.6934 | 0.04 | 10 | 5.4377 | |
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| 5.2457 | 0.06 | 15 | 4.8548 | |
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| 4.5796 | 0.08 | 20 | 4.2851 | |
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| 4.1507 | 0.1 | 25 | 3.9911 | |
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| 4.1134 | 0.12 | 30 | 3.7444 | |
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| 3.8076 | 0.14 | 35 | 3.5019 | |
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| 3.8445 | 0.16 | 40 | 3.0715 | |
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| 3.0969 | 0.18 | 45 | 2.6475 | |
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| 2.8899 | 0.2 | 50 | 2.5662 | |
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| 2.8354 | 0.22 | 55 | 2.3382 | |
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| 3.1775 | 0.24 | 60 | 2.2028 | |
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| 2.3935 | 0.26 | 65 | 2.2038 | |
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| 2.3994 | 0.28 | 70 | 1.9708 | |
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| 2.2664 | 0.3 | 75 | 1.9092 | |
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| 1.8134 | 0.32 | 80 | 1.9546 | |
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| 2.1905 | 0.34 | 85 | 1.8623 | |
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| 2.3941 | 0.36 | 90 | 1.7622 | |
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| 1.8807 | 0.38 | 95 | 1.7976 | |
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| 2.3562 | 0.4 | 100 | 1.7311 | |
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| 2.1116 | 0.42 | 105 | 1.6848 | |
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| 1.8022 | 0.44 | 110 | 1.6636 | |
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| 2.0378 | 0.46 | 115 | 1.6401 | |
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| 1.7313 | 0.48 | 120 | 1.6013 | |
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| 1.9304 | 0.5 | 125 | 1.5312 | |
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| 1.7668 | 0.52 | 130 | 1.4995 | |
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| 1.908 | 0.54 | 135 | 1.5222 | |
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| 1.9348 | 0.56 | 140 | 1.5180 | |
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| 1.7307 | 0.58 | 145 | 1.4694 | |
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| 1.9088 | 0.6 | 150 | 1.4597 | |
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| 1.3283 | 0.62 | 155 | 1.4631 | |
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| 1.6898 | 0.64 | 160 | 1.4715 | |
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| 1.7079 | 0.66 | 165 | 1.4565 | |
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| 1.6261 | 0.68 | 170 | 1.4246 | |
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| 1.5628 | 0.7 | 175 | 1.4248 | |
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| 1.7642 | 0.72 | 180 | 1.4261 | |
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| 1.5168 | 0.74 | 185 | 1.4088 | |
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| 1.5967 | 0.76 | 190 | 1.4028 | |
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| 1.275 | 0.78 | 195 | 1.4294 | |
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| 1.596 | 0.8 | 200 | 1.4128 | |
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| 1.5765 | 0.82 | 205 | 1.4032 | |
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| 1.6554 | 0.84 | 210 | 1.3599 | |
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| 1.785 | 0.86 | 215 | 1.3221 | |
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| 1.4147 | 0.88 | 220 | 1.3299 | |
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| 1.4364 | 0.9 | 225 | 1.3510 | |
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| 1.6059 | 0.92 | 230 | 1.2959 | |
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| 1.305 | 0.94 | 235 | 1.2871 | |
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| 1.4614 | 0.96 | 240 | 1.2986 | |
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| 1.3531 | 0.98 | 245 | 1.3891 | |
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| 1.3192 | 1.0 | 250 | 1.3526 | |
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| 1.0726 | 1.02 | 255 | 1.3378 | |
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| 1.1724 | 1.04 | 260 | 1.3207 | |
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| 1.2818 | 1.06 | 265 | 1.3034 | |
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| 1.1 | 1.08 | 270 | 1.2991 | |
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| 1.0719 | 1.1 | 275 | 1.2799 | |
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| 1.231 | 1.12 | 280 | 1.2880 | |
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| 1.3378 | 1.14 | 285 | 1.3066 | |
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| 1.0818 | 1.16 | 290 | 1.2954 | |
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| 1.0873 | 1.18 | 295 | 1.2754 | |
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| 1.1567 | 1.2 | 300 | 1.2741 | |
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| 1.1031 | 1.22 | 305 | 1.2502 | |
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| 1.1391 | 1.24 | 310 | 1.2674 | |
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| 1.2142 | 1.26 | 315 | 1.2849 | |
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| 0.9893 | 1.28 | 320 | 1.2841 | |
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| 1.0846 | 1.3 | 325 | 1.2748 | |
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| 1.2535 | 1.32 | 330 | 1.2628 | |
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| 1.1309 | 1.34 | 335 | 1.2410 | |
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| 0.9969 | 1.36 | 340 | 1.2267 | |
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| 1.0932 | 1.38 | 345 | 1.2032 | |
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| 1.4972 | 1.4 | 350 | 1.1923 | |
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| 0.9547 | 1.42 | 355 | 1.1954 | |
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| 1.1322 | 1.44 | 360 | 1.2043 | |
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| 0.8833 | 1.46 | 365 | 1.2234 | |
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| 0.7986 | 1.48 | 370 | 1.2600 | |
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| 1.1929 | 1.5 | 375 | 1.2788 | |
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| 0.9585 | 1.52 | 380 | 1.2554 | |
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| 1.3862 | 1.54 | 385 | 1.2165 | |
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| 1.1168 | 1.56 | 390 | 1.2064 | |
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| 1.135 | 1.58 | 395 | 1.1976 | |
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| 0.8741 | 1.6 | 400 | 1.1933 | |
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| 1.3593 | 1.62 | 405 | 1.1857 | |
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| 1.0084 | 1.64 | 410 | 1.1851 | |
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| 0.9579 | 1.66 | 415 | 1.1728 | |
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| 0.9541 | 1.68 | 420 | 1.1721 | |
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| 1.2569 | 1.7 | 425 | 1.1773 | |
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| 1.0629 | 1.72 | 430 | 1.1717 | |
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| 1.1233 | 1.74 | 435 | 1.1671 | |
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| 0.8304 | 1.76 | 440 | 1.1742 | |
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| 0.8097 | 1.78 | 445 | 1.1861 | |
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| 0.9703 | 1.8 | 450 | 1.1822 | |
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| 1.1413 | 1.82 | 455 | 1.1909 | |
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| 1.0977 | 1.84 | 460 | 1.1938 | |
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| 1.0375 | 1.86 | 465 | 1.1839 | |
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| 1.0726 | 1.88 | 470 | 1.1871 | |
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| 1.1322 | 1.9 | 475 | 1.2020 | |
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| 1.0286 | 1.92 | 480 | 1.2004 | |
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| 0.9395 | 1.94 | 485 | 1.1981 | |
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| 1.059 | 1.96 | 490 | 1.1772 | |
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| 1.0722 | 1.98 | 495 | 1.1568 | |
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| 0.8618 | 2.0 | 500 | 1.1475 | |
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| 0.9305 | 2.02 | 505 | 1.1554 | |
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| 0.8525 | 2.04 | 510 | 1.1740 | |
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| 1.0687 | 2.06 | 515 | 1.1759 | |
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| 0.8899 | 2.08 | 520 | 1.1647 | |
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| 0.6881 | 2.1 | 525 | 1.1755 | |
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| 0.8582 | 2.12 | 530 | 1.1920 | |
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| 0.6645 | 2.14 | 535 | 1.1952 | |
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| 0.6028 | 2.16 | 540 | 1.2121 | |
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| 0.7364 | 2.18 | 545 | 1.2169 | |
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| 0.5562 | 2.2 | 550 | 1.2278 | |
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| 0.6175 | 2.22 | 555 | 1.2413 | |
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| 0.5392 | 2.24 | 560 | 1.2466 | |
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| 0.8727 | 2.26 | 565 | 1.2362 | |
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| 0.6778 | 2.28 | 570 | 1.2253 | |
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| 0.685 | 2.3 | 575 | 1.2254 | |
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| 0.8991 | 2.32 | 580 | 1.2181 | |
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| 1.0157 | 2.34 | 585 | 1.2044 | |
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| 0.5054 | 2.36 | 590 | 1.1943 | |
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| 0.8036 | 2.38 | 595 | 1.1950 | |
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| 0.6207 | 2.4 | 600 | 1.2025 | |
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| 0.6828 | 2.42 | 605 | 1.2178 | |
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| 0.8008 | 2.44 | 610 | 1.2312 | |
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| 0.739 | 2.46 | 615 | 1.2401 | |
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| 0.5479 | 2.48 | 620 | 1.2459 | |
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| 0.9443 | 2.5 | 625 | 1.2359 | |
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| 0.7468 | 2.52 | 630 | 1.2264 | |
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| 0.6803 | 2.54 | 635 | 1.2223 | |
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| 0.8997 | 2.56 | 640 | 1.2208 | |
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| 0.7044 | 2.58 | 645 | 1.2118 | |
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| 0.707 | 2.6 | 650 | 1.2076 | |
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| 0.7813 | 2.62 | 655 | 1.2072 | |
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| 0.6376 | 2.64 | 660 | 1.2122 | |
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| 0.8885 | 2.66 | 665 | 1.2141 | |
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| 0.7359 | 2.68 | 670 | 1.2121 | |
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| 0.6928 | 2.7 | 675 | 1.2113 | |
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| 0.7706 | 2.72 | 680 | 1.2082 | |
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| 0.884 | 2.74 | 685 | 1.2033 | |
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| 0.6362 | 2.76 | 690 | 1.1991 | |
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| 0.8517 | 2.78 | 695 | 1.1959 | |
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| 0.7713 | 2.8 | 700 | 1.1954 | |
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| 0.8654 | 2.82 | 705 | 1.1945 | |
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| 0.6268 | 2.84 | 710 | 1.1923 | |
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| 0.8246 | 2.86 | 715 | 1.1919 | |
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| 0.646 | 2.88 | 720 | 1.1920 | |
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| 0.8648 | 2.9 | 725 | 1.1922 | |
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| 0.8398 | 2.92 | 730 | 1.1928 | |
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| 0.6281 | 2.94 | 735 | 1.1931 | |
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| 0.6319 | 2.96 | 740 | 1.1927 | |
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| 0.6304 | 2.98 | 745 | 1.1932 | |
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| 0.6554 | 3.0 | 750 | 1.1930 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
