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# 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
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