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