In this task, you are given a paragraph (from a research paper) and your task is to classify each sentence of the paragraph (assume n sentence) into the following categories: Background, Purpose, Method, Finding/Contribution, or Other. Return the output in this format: 1 - category of sentence 1, 2 - category of sentence 2, ..., n - category of sentence n; where each number indicates the order of the sentence. The categories can be identified using the following questions: 
 Background: - Why is this problem important? - What relevant works have been done before? - What did the previous works miss? - What are the high-level research questions? - How might this help other researchers?
 Purpose: - What specific things do the researchers want to do? - What specific knowledge do the researchers want to know? - What specific hypothesis do the researchers want to test?
 Method: - How did the researchers do it or find it out? - What are the procedures and steps of this research?
 Finding/Contribution: - What did the researchers find out? Did the proposed methods work? - Did things behave as the researchers expected?
Other: put every text fragment that does not fit into any of the categories above here. Put a sentence here if its not in English, is not part of the article, contains numbers and dates, is a caption of a figure or a table, is a formatting error, or you are not sure about it.

Ex Input:
In this section, we present results for the 30 countries used for model specification and tuning and compare with the overall results in order to access the extent to which tuning and model specification may have affected results for these countries. We experimented with different model specifications and combinations of features on a subset of data consisting of 30 countries, prior to applying our models to the entire collection of countries. Table 2 -2 presents the model performance metrics for both the entire collection of 72 countries and for the subset of 30 countries used for model specification and tuning. Model performance was slightly diminished on the entire collection of countries compared with the 30 countries used for model specification and tuning. In part, the poorer performance can be explained by the fact that the average number of articles per week in the entire collection of countries was lower than in the 30 countries used for model specification -0.14 articles per week versus 3.50 articles per week. We have shown that in general our models perform best on country-disease pairs that receive substantial news coverage.

Ex Output:
1 - finding, 2 - finding, 3 - finding, 4 - finding, 5 - finding, 6 - finding, 7 - finding


Ex Input:
The estimated costs and TAT were 8.2€ and 2 h for DFA, 31.8€ and 1.5 h for Simplexa™ and 55€ and 3 h for TAC testing. Conclusions: Performing the Simplexa™ test 24 h a day/7 days a week instead of DFA would considerably improve the overall sensitivity and time-to-result, albeit at a higher cost generated in the laboratory. Performing the TAC would increase the diagnostic yield and detection of co-infections significantly.

Ex Output:
1 - finding, 2 - finding, 3 - finding, 4 - finding, 5 - finding


Ex Input:
The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the nonamyloidogenic prediction accuracy is 75.00%. Conclusions: This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.

Ex Output:

1 - finding, 2 - finding, 3 - finding, 4 - finding, 5 - finding, 6 - finding, 7 - finding, 8 - finding, 9 - finding, 10 - finding, 11 - finding, 12 - finding, 13 - finding, 14 - finding, 15 - finding, 16 - finding, 17 - finding, 18 - finding