tokenizer
Browse files- merges.txt +0 -0
- scripts/train_tokenizer.py +70 -41
- tokenizer.json +0 -0
- tokenizer_config.json +569 -57
- vocab.json +0 -0
merges.txt
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scripts/train_tokenizer.py
CHANGED
@@ -151,36 +151,36 @@ def batch_iterator():
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del dataset
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gc.collect()
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bpe = BPE(unk_token='<unk>', fuse_unk=True, byte_fallback=True)
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@@ -192,40 +192,69 @@ special_tokens = [
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'</s>',
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'<|im_start|>',
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'<|im_end|>',
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'<tools>',
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'</tools>',
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'<tool_call>',
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'</tool_call>',
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'<tool_response>',
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'</tool_response>',
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'user',
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'assistant',
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'tool',
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'"arguments"',
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'<arguments>',
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'<argument>',
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'<argument-name>',
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'<parameter>',
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'<parameter-name>',
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'
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'<
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'<parameter-value>',
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'
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'<function>',
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'<function-name>',
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]
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for i in range(2, 25):
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special_tokens.append(' ' * i)
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for i in range(
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special_tokens.append(f'<|reserved_{i}|>')
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## ascii
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# ascii_chars = list(string.ascii_letters + string.ascii_lowercase + string.ascii_uppercase + string.digits + string.punctuation)
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-
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# emoji
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dataset = load_dataset('badrex/llm-emoji-dataset', split='train')
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emoji_chars = [row['character'] for row in dataset if len(row['character']) == 1]
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@@ -252,7 +281,7 @@ tokenizer.post_processor = TemplateProcessing(
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tokenizer.decoder = decoders.ByteLevel(add_prefix_space=False, trim_offsets=True, use_regex=True)
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trainer = BpeTrainer(
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vocab_size=
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min_frequency=2,
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special_tokens=special_tokens,
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initial_alphabet=emoji_chars + programming_languages + code_keywords,
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del dataset
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gc.collect()
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# instruction
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dataset = load_dataset('arcee-ai/agent-data', split='train')
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for row in dataset['conversations']:
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yield '\n'.join(n['value'] for n in row)
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del dataset
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gc.collect()
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# instruction
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dataset = (
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load_dataset('cognitivecomputations/SystemChat-2.0', data_files='SystemChat_filtered.jsonl', split='train'),
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load_dataset('cognitivecomputations/SystemChat-2.0', data_files='SystemChat_multilingual.jsonl', split='train'),
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)
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for d in dataset:
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for row in d['messages']:
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yield '\n'.join(n['content'] for n in row)
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del dataset
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gc.collect()
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# emoji
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dataset = load_dataset('badrex/llm-emoji-dataset', split='train')
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for row in dataset:
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yield f'{row["character"]}\n{row["unicode"]}\n{row["short description"]}\n{row["tags"]}\n{row["LLM description"]}'
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del dataset
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gc.collect()
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bpe = BPE(unk_token='<unk>', fuse_unk=True, byte_fallback=True)
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'</s>',
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'<|im_start|>',
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'<|im_end|>',
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'system',
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'user',
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'assistant',
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'tool',
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'<tools>',
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'</tools>',
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'<tool_call>',
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'</tool_call>',
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'<tool_response>',
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'</tool_response>',
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'"arguments"',
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'"name"',
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'<arguments>',
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'</arguments>',
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'<argument>',
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'</argument>',
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'<argument-name>',
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'</argument-name>',
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'<argument-type>',
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'</argument-type>',
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'<argument-value>',
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'</argument-value>',
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'<parameter>',
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'</parameter>',
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'<parameter-name>',
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'</parameter-name>',
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'<parameter-type>',
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'</parameter-type>',
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'<parameter-value>',
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'</parameter-value>',
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'<field>',
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'</field>',
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'<field-name>',
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'</field-name>',
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'<field-type>',
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'</field-type>',
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'<field-value>',
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'</field-value>',
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'<name>',
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'</name>',
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'<type>',
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'</type>',
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'<value>',
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'</value>',
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'<function>',
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'</function>',
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'<function-name>',
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'</function-name>',
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'<function-type>',
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'</function-type>',
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'<function-value>',
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'</function-value>',
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]
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for i in range(2, 25):
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special_tokens.append(' ' * i)
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for i in range(128 - len(special_tokens)):
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special_tokens.append(f'<|reserved_{i}|>')
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# emoji
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dataset = load_dataset('badrex/llm-emoji-dataset', split='train')
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emoji_chars = [row['character'] for row in dataset if len(row['character']) == 1]
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tokenizer.decoder = decoders.ByteLevel(add_prefix_space=False, trim_offsets=True, use_regex=True)
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trainer = BpeTrainer(
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vocab_size=32768, # 2 ** 15
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min_frequency=2,
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special_tokens=special_tokens,
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initial_alphabet=emoji_chars + programming_languages + code_keywords,
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tokenizer.json
CHANGED
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tokenizer_config.json
CHANGED
@@ -41,7 +41,7 @@
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"special": true
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},
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"5": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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"6": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"special": true
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"special": true
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"special": true
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"9": {
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"content": "<
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"10": {
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"content": "</
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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"11": {
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"special": true
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"12": {
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"special": true
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"special": true
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"special": true
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"16": {
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"special": true
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},
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"17": {
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"content": "<
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"lstrip": false,
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"normalized": false,
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"special": true
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"18": {
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"special": true
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"19": {
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"special": true
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"20": {
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"special": true
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"21": {
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"special": true
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"22": {
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"special": true
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"23": {
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"24": {
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"25": {
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"26": {
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},
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"28": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -369,7 +369,7 @@
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"special": true
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},
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"46": {
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"content": "
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"normalized": false,
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"special": true
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"47": {
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"special": true
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"48": {
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"special": true
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"49": {
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"content": "
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"50": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"51": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"52": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"53": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"54": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"special": true
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},
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"55": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -449,7 +449,7 @@
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"special": true
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},
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"56": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -457,7 +457,7 @@
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"special": true
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},
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"57": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -465,7 +465,7 @@
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"special": true
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},
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"58": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -473,7 +473,7 @@
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"special": true
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},
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"59": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -481,7 +481,7 @@
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"special": true
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},
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"60": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -489,7 +489,7 @@
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"special": true
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},
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"61": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -497,7 +497,7 @@
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"special": true
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},
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"62": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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@@ -505,12 +505,524 @@
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"special": true
|
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},
|
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"63": {
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508 |
"content": "<|reserved_13|>",
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510 |
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|
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|
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514 |
}
|
515 |
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|
516 |
"bos_token": "<s>",
|
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|
41 |
"special": true
|
42 |
},
|
43 |
"5": {
|
44 |
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"content": "system",
|
45 |
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|
46 |
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|
47 |
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|
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|
49 |
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50 |
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51 |
"6": {
|
52 |
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"content": "user",
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53 |
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|
54 |
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|
55 |
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57 |
"special": true
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58 |
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59 |
"7": {
|
60 |
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"content": "assistant",
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61 |
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|
62 |
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|
63 |
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|
65 |
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66 |
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67 |
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|
68 |
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69 |
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|
70 |
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|
71 |
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|
73 |
"special": true
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74 |
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75 |
"9": {
|
76 |
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"content": "<tools>",
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77 |
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|
78 |
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|
79 |
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|
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|
81 |
"special": true
|
82 |
},
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83 |
"10": {
|
84 |
+
"content": "</tools>",
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85 |
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|
86 |
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|
87 |
"rstrip": false,
|
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|
89 |
"special": true
|
90 |
},
|
91 |
"11": {
|
92 |
+
"content": "<tool_call>",
|
93 |
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|
94 |
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|
95 |
"rstrip": false,
|
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|
97 |
"special": true
|
98 |
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|
99 |
"12": {
|
100 |
+
"content": "</tool_call>",
|
101 |
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|
102 |
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|
103 |
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|
105 |
"special": true
|
106 |
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|
107 |
"13": {
|
108 |
+
"content": "<tool_response>",
|
109 |
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|
110 |
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|
111 |
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|
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|
113 |
"special": true
|
114 |
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|
115 |
"14": {
|
116 |
+
"content": "</tool_response>",
|
117 |
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|
118 |
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|
119 |
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|
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|
129 |
"special": true
|
130 |
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|
131 |
"16": {
|
132 |
+
"content": "\"name\"",
|
133 |
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|
134 |
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|
135 |
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|
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|
137 |
"special": true
|
138 |
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|
139 |
"17": {
|
140 |
+
"content": "<arguments>",
|
141 |
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|
142 |
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|
143 |
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|
|
|
145 |
"special": true
|
146 |
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|
147 |
"18": {
|
148 |
+
"content": "</arguments>",
|
149 |
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|
150 |
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|
151 |
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|
|
|
153 |
"special": true
|
154 |
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|
155 |
"19": {
|
156 |
+
"content": "<argument>",
|
157 |
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|
158 |
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|
159 |
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|
|
|
161 |
"special": true
|
162 |
},
|
163 |
"20": {
|
164 |
+
"content": "</argument>",
|
165 |
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|
166 |
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|
167 |
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|
|
|
169 |
"special": true
|
170 |
},
|
171 |
"21": {
|
172 |
+
"content": "<argument-name>",
|
173 |
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|
174 |
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|
175 |
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|
|
|
177 |
"special": true
|
178 |
},
|
179 |
"22": {
|
180 |
+
"content": "</argument-name>",
|
181 |
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|
182 |
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|
183 |
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|
|
|
185 |
"special": true
|
186 |
},
|
187 |
"23": {
|
188 |
+
"content": "<argument-type>",
|
189 |
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|
190 |
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|
191 |
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|
|
|
193 |
"special": true
|
194 |
},
|
195 |
"24": {
|
196 |
+
"content": "</argument-type>",
|
197 |
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|
198 |
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|
199 |
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|
|
|
201 |
"special": true
|
202 |
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|
203 |
"25": {
|
204 |
+
"content": "<argument-value>",
|
205 |
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|
206 |
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|
207 |
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|
|
|
209 |
"special": true
|
210 |
},
|
211 |
"26": {
|
212 |
+
"content": "</argument-value>",
|
213 |
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|
214 |
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|
215 |
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|
|
|
217 |
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|
218 |
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|
219 |
"27": {
|
220 |
+
"content": "<parameter>",
|
221 |
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|
222 |
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|
223 |
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|
|
|
225 |
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|
226 |
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|
227 |
"28": {
|
228 |
+
"content": "</parameter>",
|
229 |
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|
230 |
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|
231 |
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|
|
|
233 |
"special": true
|
234 |
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|
235 |
"29": {
|
236 |
+
"content": "<parameter-name>",
|
237 |
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|
238 |
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|
239 |
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|
|
|
241 |
"special": true
|
242 |
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|
243 |
"30": {
|
244 |
+
"content": "</parameter-name>",
|
245 |
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|
246 |
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|
247 |
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|
|
|
249 |
"special": true
|
250 |
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|
251 |
"31": {
|
252 |
+
"content": "<parameter-type>",
|
253 |
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|
254 |
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|
255 |
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|
|
|
257 |
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|
258 |
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|
259 |
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|
260 |
+
"content": "</parameter-type>",
|
261 |
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|
262 |
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|
263 |
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|
|
|
265 |
"special": true
|
266 |
},
|
267 |
"33": {
|
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903 |
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909 |
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910 |
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|
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914 |
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|
916 |
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920 |
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|
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|
924 |
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925 |
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|
930 |
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|
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|
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938 |
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|
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950 |
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|
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954 |
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978 |
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993 |
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|
1000 |
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|
1001 |
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1002 |
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1003 |
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1004 |
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1005 |
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1006 |
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1007 |
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1008 |
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1009 |
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1010 |
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1011 |
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|
1012 |
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1013 |
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|
1014 |
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|
1015 |
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|
1016 |
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|
1017 |
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1018 |
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1019 |
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|
1020 |
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1021 |
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|
1022 |
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|
1023 |
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|
1024 |
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|
1025 |
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|
1026 |
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|
1027 |
},
|
1028 |
"bos_token": "<s>",
|
vocab.json
CHANGED
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|
|