chars, wiki, comments v0.1

README.md

@@ -2,6 +2,11 @@
 We will release a better one soon:)
 
 <br><br>
+## train details:
+**token**: char based (1000K vocab) <br>
+**lm dataset**: wiki <br>
+**sentiment dataset**: comments on news
+
 
 ## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
 HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805). <br>

config.json

@@ -7,15 +7,25 @@
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
+  "id2label": {
+    "0": "natural",
+    "1": "possitive",
+    "2": "negetive"
+  },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
+  "label2id": {
+    "natural": 0,
+    "negetive": 2,
+    "possitive": 1
+  },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 514,
   "model_type": "bert",
   "num_attention_heads": 12,
   "num_hidden_layers": 6,
   "pad_token_id": 0,
-  "total_flos": 3118796722622472192,
+  "total_flos": 451483328659341312,
   "type_vocab_size": 1,
   "vocab_size": 52000
 }

pytorch_model.bin

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training_args.bin

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