readme: # bert-base-buddhist-sanskrit
  
  Version 2 of the BERT model described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (https://aclanthology.org/2022.lrec-1.411/). 
  Same training methodology has been used as for version 1, the only difference is that the model has been trained on a slightly bigger buddhist snaskrit corpus.
  
  ## 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: 24
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 200
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.9.0
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  
  