# 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


{}
transformers pytorch bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# bert-base-buddhist-sanskrit

Version 2 of the BERT model described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (URL 
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



## 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


# bert-base-buddhist-sanskrit

Version 2 of the BERT model described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (URL 
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 ## 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
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-buddhist-sanskrit

Version 2 of the BERT model described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (URL 
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 ## 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
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-buddhist-sanskrit

Version 2 of the BERT model described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (URL 
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## 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