---
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
---

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# 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