|
--- |
|
license: mit |
|
base_model: xlm-roberta-large |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: SCIFACT_inference_model |
|
results: [] |
|
datasets: |
|
- allenai/scifact |
|
language: |
|
- en |
|
widget: |
|
- text: "[CLS]A country's Vaccine Alliance (GAVI) eligibility is indictivate of accelerated adoption of the Hub vaccine.[SEP]Accelerating Policy Decisions to Adopt Haemophilus influenzae Type b Vaccine: A Global, Multivariable Analysis BACKGROUND Adoption of new and underutilized vaccines by national immunization programs is an essential step towards reducing child mortality. Policy decisions to adopt new vaccines in high mortality countries often lag behind decisions in high-income countries. Using the case of Haemophilus influenzae type b (Hib) vaccine, this paper endeavors to explain these delays through the analysis of country-level economic, epidemiological, programmatic and policy-related factors, as well as the role of the Global Alliance for Vaccines and Immunisation (GAVI Alliance). METHODS AND FINDINGS Data for 147 countries from 1990 to 2007 were analyzed in accelerated failure time models to identify factors that are associated with the time to decision to adopt Hib vaccine. In multivariable models that control for Gross National Income, region, and burden of Hib disease, the receipt of GAVI support speeded the time to decision by a factor of 0.37 (95% CI 0.18-0.76), or 63%. The presence of two or more neighboring country adopters accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33-0.75). For each 1% increase in vaccine price, decisions to adopt are delayed by a factor of 1.02 (95% CI 1.00-1.04). Global recommendations and local studies were not associated with time to decision.CONCLUSIONS This study substantiates previous findings related to vaccine price and presents new evidence to suggest that GAVI eligibility is associated with accelerated decisions to adopt Hib vaccine. The influence of neighboring country decisions was also highly significant, suggesting that approaches to support the adoption of new vaccines should consider supply- and demand-side factors. " |
|
library_name: transformers |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# SCIFACT_inference_model |
|
|
|
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2496 |
|
- Accuracy: 0.8819 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 3 |
|
- eval_batch_size: 3 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 1.0 | 378 | 1.0485 | 0.4724 | |
|
| 1.0382 | 2.0 | 756 | 1.3964 | 0.6063 | |
|
| 0.835 | 3.0 | 1134 | 0.9168 | 0.8268 | |
|
| 0.6801 | 4.0 | 1512 | 0.7524 | 0.8425 | |
|
| 0.6801 | 5.0 | 1890 | 1.0672 | 0.8346 | |
|
| 0.4291 | 6.0 | 2268 | 0.9599 | 0.8425 | |
|
| 0.2604 | 7.0 | 2646 | 0.8691 | 0.8661 | |
|
| 0.1932 | 8.0 | 3024 | 1.3162 | 0.8268 | |
|
| 0.1932 | 9.0 | 3402 | 1.3200 | 0.8583 | |
|
| 0.0974 | 10.0 | 3780 | 1.1566 | 0.8740 | |
|
| 0.1051 | 11.0 | 4158 | 1.1568 | 0.8819 | |
|
| 0.0433 | 12.0 | 4536 | 1.2013 | 0.8661 | |
|
| 0.0433 | 13.0 | 4914 | 1.1557 | 0.8819 | |
|
| 0.034 | 14.0 | 5292 | 1.3044 | 0.8661 | |
|
| 0.0303 | 15.0 | 5670 | 1.2496 | 0.8819 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.1 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
