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--- |
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tags: |
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- Buddhist Sanskrit |
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- BERT |
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- name: bert-base-buddhist-sanskrit |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-buddhist-sanskrit |
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The best performing model of the research described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (Link to the paper will be added after |
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the publication of conference proceedings). |
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## Model description |
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The model has the bert-base architecture and configuration and was pretrained from scratch as a masked language model |
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on the Sanskrit reference corpus, and fine-tuned on the smaller corpus of Buddhist Sanskrit. |
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## How to use it |
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``` |
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model = AutoModelForMaskedLM.from_pretrained("Matej/bert-base-buddhist-sanskrit") |
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tokenizer = AutoTokenizer.from_pretrained("Matej/bert-base-buddhist-sanskrit", use_fast=True) |
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``` |
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## Intended uses & limitations |
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MIT license, no limitations |
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## Training and evaluation data |
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See the paper 'Embeddings models for Buddhist Sanskrit' for details on the corpora and the evaluation procedure. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 28 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 300.0 |
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### Framework versions |
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- Transformers 4.11.2 |
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- Pytorch 1.7.0 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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