Training in progress, step 500

.ipynb_checkpoints/config-checkpoint.json

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.ipynb_checkpoints/vocab-checkpoint.json

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added_tokens.json

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+{"<s>": 36, "</s>": 37}

config.json

@@ -49,7 +49,7 @@
   "feat_extract_activation": "gelu",
   "feat_extract_dropout": 0.0,
   "feat_extract_norm": "layer",
-  "feat_proj_dropout": 0.0,
+  "feat_proj_dropout": 0.01,
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   "final_dropout": 0.0,
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+  "layerdrop": 0.01,
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   "mask_feature_prob": 0.25,
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   "num_hidden_layers": 24,
   "num_negatives": 100,
   "output_hidden_size": 1024,
-  "pad_token_id": 43,
+  "pad_token_id": 35,
   "proj_codevector_dim": 768,
   "tdnn_dilation": [
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   "transformers_version": "4.16.0.dev0",
   "use_weighted_layer_sum": false,
-  "vocab_size": 46,
+  "vocab_size": 38,
   "xvector_output_dim": 512
 }

eval.py

@@ -49,7 +49,7 @@ def log_results(result: Dataset, args: Dict[str, str]):
 def normalize_text(text: str) -> str:
     """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
 
-    chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
+    chars_to_ignore_regex = '[!"%,.:;?\\_|©«¬»،؛؟‒–—’“”„…‹›−☺♂�\\\\-]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
 
     text = re.sub(chars_to_ignore_regex, "", text.lower())
 

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special_tokens_map.json

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tokenizer_config.json

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training_args.bin

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vocab.json

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