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# bert-small-buddhist-nonbuddhist-sanskrit
BERT model trained on a lemmatized corpus containing Buddhist and non-Buddhist Sanskrit texts.
## Model description
The model has the bert architecture and was pretrained from scratch as a masked language model
on the lemmatized Sanskrit corpus. Due to lack of resources and to prevent overfitting, the model is smaller than bert-base,
i.e. the number of attention heads and hidden layers have been reduced to 8 and the context has been reduced to 128 tokens. Vocabulary size is 10000 tokens.
## How to use it
```
model = AutoModelForMaskedLM.from_pretrained("Matej/bert-small-buddhist-nonbuddhist-sanskrit")
tokenizer = AutoTokenizer.from_pretrained("Matej/bert-small-buddhist-nonbuddhist-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: 32
- 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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