First version of tokenizer and basic pytorch model.

README.md

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+---
+language:
+- he 
+tags:
+- language model
+license: apache-2.0
+datasets:
+- oscar
+- wikipedia
+- twitter 
+---
+
+# AlephBERT
+
+## Hebrew Language Model
+
+State-of-the-art language model for Hebrew. Based on BERT.
+
+#### How to use
+
+```python
+from transformers import BertModel, BertTokenizerFast
+
+alephbert_tokenizer = BertTokenizerFast.from_pretrained('onlplab/alephbert-base')
+alephbert = BertModel.from_pretrained('onlplab/alephbert-base')
+
+# if not finetuning - disable dropout
+alephbert.eval()
+```
+
+## Training data
+
+- OSCAR (10G text, 20M sentences)
+- Wikipedia dump (0.6G text, 3M sentences)
+- Tweets (7G text, 70M sentences)
+
+## Training procedure
+
+Trained on a DGX machine (8 V100 GPUs) using the standard huggingface training procedure.
+
+To optimize training time we split the data into 4 sections based on max number of tokens:
+
+1. num tokens < 32 (70M sentences)
+2. 32 <= num tokens < 64 (12M sentences)
+3. 64 <= num tokens < 128 (10M sentences)
+4. 128 <= num tokens < 512 (70M sentences)
+
+Each section was trained for 5 epochs with an initial learning rate set to 1e-4.
+
+Total training time was 5 days.
+
+## Eval
+
+

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.2.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 52000
+}

pytorch_model.bin

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+size 504210578

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "max_len": 512, "special_tokens_map_file": null, "do_basic_tokenize": true, "never_split": null}

training_args.bin

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