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--- |
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license: agpl-3.0 |
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base_model: xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: SCIFACT_inference_model |
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results: [] |
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datasets: |
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- allenai/scifact |
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language: |
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- en |
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widget: |
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- text: >- |
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[CLS]A country's Vaccine Alliance (GAVI) eligibility is indictivate of |
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accelerated adoption of the Hub vaccine.[SEP]Accelerating Policy Decisions |
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to Adopt Haemophilus influenzae Type b Vaccine: A Global, Multivariable |
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Analysis BACKGROUND Adoption of new and underutilized vaccines by national |
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immunization programs is an essential step towards reducing child mortality. |
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Policy decisions to adopt new vaccines in high mortality countries often lag |
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behind decisions in high-income countries. Using the case of Haemophilus |
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influenzae type b (Hib) vaccine, this paper endeavors to explain these |
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delays through the analysis of country-level economic, epidemiological, |
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programmatic and policy-related factors, as well as the role of the Global |
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Alliance for Vaccines and Immunisation (GAVI Alliance). METHODS AND FINDINGS |
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Data for 147 countries from 1990 to 2007 were analyzed in accelerated |
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failure time models to identify factors that are associated with the time to |
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decision to adopt Hib vaccine. In multivariable models that control for |
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Gross National Income, region, and burden of Hib disease, the receipt of |
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GAVI support speeded the time to decision by a factor of 0.37 (95% CI |
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0.18-0.76), or 63%. The presence of two or more neighboring country adopters |
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accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33-0.75). For |
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each 1% increase in vaccine price, decisions to adopt are delayed by a |
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factor of 1.02 (95% CI 1.00-1.04). Global recommendations and local studies |
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were not associated with time to decision.CONCLUSIONS This study |
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substantiates previous findings related to vaccine price and presents new |
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evidence to suggest that GAVI eligibility is associated with accelerated |
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decisions to adopt Hib vaccine. The influence of neighboring country |
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decisions was also highly significant, suggesting that approaches to support |
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the adoption of new vaccines should consider supply- and demand-side |
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factors. |
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library_name: transformers |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# SCIFACT_inference_model |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the SciFact dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2496 |
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- Accuracy: 0.8819 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 3 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 378 | 1.0485 | 0.4724 | |
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| 1.0382 | 2.0 | 756 | 1.3964 | 0.6063 | |
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| 0.835 | 3.0 | 1134 | 0.9168 | 0.8268 | |
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| 0.6801 | 4.0 | 1512 | 0.7524 | 0.8425 | |
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| 0.6801 | 5.0 | 1890 | 1.0672 | 0.8346 | |
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| 0.4291 | 6.0 | 2268 | 0.9599 | 0.8425 | |
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| 0.2604 | 7.0 | 2646 | 0.8691 | 0.8661 | |
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| 0.1932 | 8.0 | 3024 | 1.3162 | 0.8268 | |
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| 0.1932 | 9.0 | 3402 | 1.3200 | 0.8583 | |
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| 0.0974 | 10.0 | 3780 | 1.1566 | 0.8740 | |
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| 0.1051 | 11.0 | 4158 | 1.1568 | 0.8819 | |
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| 0.0433 | 12.0 | 4536 | 1.2013 | 0.8661 | |
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| 0.0433 | 13.0 | 4914 | 1.1557 | 0.8819 | |
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| 0.034 | 14.0 | 5292 | 1.3044 | 0.8661 | |
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| 0.0303 | 15.0 | 5670 | 1.2496 | 0.8819 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
