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---
license: agpl-3.0
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 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 |