Andhra Pradesh, Karnataka, Maharashtra, Tamil Nadu and Telangana states of India. A pretested, structured questionnaire End result Monitoring Survey instrument was used to collect data instantly on a digital Pill. In addition to the fundamental demographic and socio-economic characteristics of the respondents, the detailed questionnaire also captured quite a lot of different components together with CO membership, sexual habits, condom use with purchasers and intimate partners, historical past of STI (Sexually transmitted infections) during final six months, HIV testing behavior, entry to and protection with gender based mostly violence, social safety, (civic identities and social schemes), financial safety companies and products (Financial savings Account, Savings Products, insurance and different financial schemes), reproductive well being standing as well as monetary and meals disaster or insufficiency skilled by respondents.

They also consist of 182 cross-lingual word embedding fashions for each pair. Nevertheless, as a result of differences in language properties in addition to corpora sizes, the quality of the fashions range. Be aware that the NER tagged dataset was not obtainable for Telugu, in order that they couldn’t be compared on this process. Table 1 shows the language sensible corpus statistics. This also serves to spotlight the difference between the properties of these two languages. Telugu fashions consistently outperform Tamil models on all frequent tasks. Analysis of the fashions has already been presented in Section 4.8.. An attention-grabbing point is that regardless that Tamil and Telugu have comparable corpora sizes, the evaluations of their word embeddings show completely different outcomes.

Provide them with our repository.

We create these models. The training for these models took 2 days over 1 x 2080Ti GPU (12 GB). 14) and thus produce 196 models using this method and provide them in our repository. Provide them with our repository. Resulting from resource constraints and the fact that cross-lingual representations require a large amount of knowledge, we choose to train 50-dimensional embeddings for each language pair. MUSE embeddings are cross-lingual embeddings that can be skilled utilizing the fastText embeddings, which we had created previously. 512 dimensions. These vectors are realized functions of the internal states of a deep bidirectional language mannequin (biLM). The training time for each language corpus was approximately 1 day on a 12 GB Nvidia GeForce GTX TitanX GPU.

12 GB Tesla K80 GPUs – related resource site – . The official repository for BERT gives a multilingual mannequin of 102 languages, which incorporates all but four (Oriya, Assamese, Sanskrit, Konkani) of the 14 languages. We provide a single multilingual BERT mannequin for all of the 14 languages, including these 4 languages. We prepare this mannequin for 300-dimensional embeddings. The corpus vocabulary measurement of 25000 was chosen. Over the standard hyperparameters as described with their work.

India. Nonetheless, data that is readily out there for computational functions has been excruciatingly limited, even for these 14 languages. Certainly one of the most important contributions of this paper is the accumulation of data in a single repository. As a baseline dataset, we first extract textual content from Wikipedia dumps444As on fifteenth August, 2019, and then append the information from different sources onto it. Hindi because the source language in tourism and health domains.