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First model version

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README.md ADDED
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+ ---
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+ tags:
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+ - Buddhist Sanskrit
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+ - BERT
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+ - name: bert-base-buddhist-sanskrit
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-base-buddhist-sanskrit
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+
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+ 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
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+ the publication of conference proceedings).
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+
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+ ## Model description
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+
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+ The model has the bert-base architecture and configuration and was pretrained from scratch as a masked language model
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+ on the Sanskrit reference corpus, and fine-tuned on the smaller corpus of Buddhist Sanskrit.
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+
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+ ## Intended uses & limitations
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+
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+ MIT license, no limitations
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+
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+ ## Training and evaluation data
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+
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+ See the paper 'Embeddings models for Buddhist Sanskrit' for details on the corpora and the evaluation procedure.
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 28
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 300.0
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+
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+ ### Framework versions
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+
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+ - Transformers 4.11.2
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+ - Pytorch 1.7.0
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+ - Datasets 1.12.1
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+ - Tokenizers 0.10.3
config.json ADDED
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+ {
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+ "_name_or_path": "models_reference_all_tokens",
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+ "architectures": [
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+ "BertForMaskedLM"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.11.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30000
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+ }
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special_tokens_map.json ADDED
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {"special_tokens_map_file": "models_reference_all_tokens/special_tokens_map.json", "name_or_path": "models_reference_all_tokens", "tokenizer_class": "PreTrainedTokenizerFast"}