|
--- |
|
license: agpl-3.0 |
|
base_model: xlm-roberta-large |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
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 |
|
pipeline_tag: text-classification |
|
--- |
|
|
|
<!-- 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 SciFact 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 |