transformers pytorch xlm-roberta text-classification generated_from_trainer en dataset:allenai/scifact base_model:FacebookAI/xlm-roberta-large base_model:finetune:FacebookAI/xlm-roberta-large license:agpl-3.0 autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-large
datasets:
- allenai/scifact
language:
- en
library_name: transformers
license: agpl-3.0
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
tags:
- generated_from_trainer
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. '
model-index:
- name: SCIFACT_inference_model
  results: []
---

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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