init
Browse files- config.json +202 -0
- parameter.json +1 -0
- pytorch_model.bin +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +1 -0
- test_bc5cdr_lower.json +1 -0
- test_bc5cdr_span_lower.json +1 -0
- test_bionlp2004_lower.json +1 -0
- test_bionlp2004_span_lower.json +1 -0
- test_conll2003_lower.json +1 -0
- test_conll2003_span_lower.json +1 -0
- test_fin_lower.json +1 -0
- test_fin_span_lower.json +1 -0
- test_mit_movie_trivia_lower.json +1 -0
- test_mit_movie_trivia_span_lower.json +1 -0
- test_mit_restaurant_lower.json +1 -0
- test_mit_restaurant_span_lower.json +1 -0
- test_ontonotes5_lower.json +1 -0
- test_ontonotes5_span_lower.json +1 -0
- test_panx_dataset-en_lower.json +1 -0
- test_panx_dataset-en_span_lower.json +1 -0
- test_wnut2017_lower.json +1 -0
- test_wnut2017_span_lower.json +1 -0
- tokenizer_config.json +1 -0
config.json
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{
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"_name_or_path": "xlm-roberta-base",
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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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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"id2label": {
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"0": "B-organization",
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"1": "O",
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"2": "B-other",
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"3": "B-person",
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"4": "I-person",
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"5": "B-location",
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"6": "I-organization",
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"7": "I-other",
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"8": "I-location",
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"9": "B-cardinal number",
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"10": "B-date",
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"11": "I-date",
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"12": "B-group",
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"13": "B-geopolitical area",
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"14": "I-geopolitical area",
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"15": "B-law",
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"16": "I-law",
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"17": "B-percent",
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"18": "I-percent",
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"19": "B-ordinal number",
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"20": "B-money",
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"21": "I-money",
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"22": "B-work of art",
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"23": "I-work of art",
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"24": "B-facility",
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"25": "B-time",
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"26": "I-cardinal number",
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"27": "B-quantity",
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"28": "I-quantity",
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"29": "I-group",
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"30": "B-product",
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"31": "I-time",
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"32": "B-event",
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"33": "I-event",
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"34": "I-facility",
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"35": "B-language",
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"36": "I-product",
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"37": "I-ordinal number",
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"38": "I-language",
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"39": "B-actor",
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"40": "I-actor",
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"41": "B-plot",
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"42": "I-plot",
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"43": "B-opinion",
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"44": "I-opinion",
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"45": "B-award",
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"46": "I-award",
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"47": "B-genre",
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"48": "B-origin",
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"49": "I-origin",
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"50": "B-director",
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"51": "I-director",
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"52": "I-genre",
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"53": "B-soundtrack",
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"54": "I-soundtrack",
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"55": "B-relationship",
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"56": "I-relationship",
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"57": "B-character name",
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"58": "I-character name",
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"59": "B-quote",
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"60": "I-quote",
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"61": "B-rating",
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"62": "I-rating",
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"63": "B-amenity",
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"64": "I-amenity",
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"65": "B-restaurant",
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"66": "I-restaurant",
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"67": "B-dish",
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"68": "I-dish",
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"69": "B-cuisine",
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"70": "I-cuisine",
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"71": "B-chemical",
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"72": "B-disease",
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"73": "I-disease",
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"74": "I-chemical",
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"75": "B-dna",
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"76": "I-dna",
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"77": "B-protein",
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"78": "I-protein",
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"79": "B-cell type",
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"80": "I-cell type",
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"81": "B-cell line",
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"82": "I-cell line",
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"83": "B-rna",
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"84": "I-rna",
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"85": "B-corporation",
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"86": "I-corporation"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-actor": 39,
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"B-amenity": 63,
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"B-award": 45,
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"B-cardinal number": 9,
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"B-cell line": 81,
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"B-cell type": 79,
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"B-character name": 57,
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"B-chemical": 71,
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"B-corporation": 85,
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"B-cuisine": 69,
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"B-date": 10,
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"B-director": 50,
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"B-disease": 72,
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118 |
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"B-dish": 67,
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"B-dna": 75,
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120 |
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"B-event": 32,
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121 |
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"B-facility": 24,
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122 |
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"B-genre": 47,
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"B-geopolitical area": 13,
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124 |
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"B-group": 12,
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"B-language": 35,
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126 |
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"B-law": 15,
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"B-location": 5,
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128 |
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"B-money": 20,
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129 |
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"B-opinion": 43,
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130 |
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"B-ordinal number": 19,
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131 |
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"B-organization": 0,
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"B-origin": 48,
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133 |
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"B-other": 2,
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"B-percent": 17,
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135 |
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"B-person": 3,
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"B-plot": 41,
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137 |
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"B-product": 30,
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138 |
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"B-protein": 77,
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139 |
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"B-quantity": 27,
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"B-quote": 59,
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"B-rating": 61,
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"B-relationship": 55,
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"B-restaurant": 65,
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"B-rna": 83,
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"B-soundtrack": 53,
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"B-time": 25,
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147 |
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"B-work of art": 22,
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148 |
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"I-actor": 40,
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149 |
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"I-amenity": 64,
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150 |
+
"I-award": 46,
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"I-cardinal number": 26,
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152 |
+
"I-cell line": 82,
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153 |
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"I-cell type": 80,
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154 |
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"I-character name": 58,
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155 |
+
"I-chemical": 74,
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156 |
+
"I-corporation": 86,
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157 |
+
"I-cuisine": 70,
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158 |
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"I-date": 11,
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159 |
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"I-director": 51,
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160 |
+
"I-disease": 73,
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161 |
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"I-dish": 68,
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162 |
+
"I-dna": 76,
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163 |
+
"I-event": 33,
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164 |
+
"I-facility": 34,
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165 |
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"I-genre": 52,
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166 |
+
"I-geopolitical area": 14,
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167 |
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"I-group": 29,
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+
"I-language": 38,
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"I-law": 16,
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"I-location": 8,
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"I-money": 21,
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+
"I-opinion": 44,
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173 |
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"I-ordinal number": 37,
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174 |
+
"I-organization": 6,
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175 |
+
"I-origin": 49,
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176 |
+
"I-other": 7,
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177 |
+
"I-percent": 18,
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"I-person": 4,
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+
"I-plot": 42,
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+
"I-product": 36,
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+
"I-protein": 78,
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182 |
+
"I-quantity": 28,
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183 |
+
"I-quote": 60,
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"I-rating": 62,
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"I-relationship": 56,
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+
"I-restaurant": 66,
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"I-rna": 84,
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+
"I-soundtrack": 54,
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189 |
+
"I-time": 31,
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190 |
+
"I-work of art": 23,
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191 |
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"O": 1
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192 |
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},
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193 |
+
"layer_norm_eps": 1e-05,
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194 |
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"max_position_embeddings": 514,
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"model_type": "xlm-roberta",
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196 |
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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198 |
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"output_past": true,
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199 |
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"pad_token_id": 1,
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200 |
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"type_vocab_size": 1,
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"vocab_size": 250002
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}
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parameter.json
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{"dataset": ["conll2003", "ontonotes5", "mit_movie_trivia", "mit_restaurant", "bc5cdr", "bionlp2004", "fin", "wnut2017", "panx_dataset/en"], "transformers_model": "xlm-roberta-base", "random_seed": 1234, "lr": 1e-05, "total_step": 15000, "warmup_step": 700, "weight_decay": 1e-07, "batch_size": 32, "max_seq_length": 128, "fp16": false, "max_grad_norm": 1.0, "lower_case": true, "checkpoint_prefix": null}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:357433282d8fee14db156ab5de196d9d25dcc02fcb6315f6f2c92a14092eefc9
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size 1110165157
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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size 5069051
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special_tokens_map.json
ADDED
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": "<mask>"}
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test_bc5cdr_lower.json
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{"valid": {"f1": 79.70983797521973, "recall": 78.84152089661673, "precision": 80.5974943784131, "summary": " precision recall f1-score support\n\n chemical 0.87 0.82 0.84 5324\n disease 0.73 0.75 0.74 4223\n\n micro avg 0.81 0.79 0.80 9547\n macro avg 0.80 0.78 0.79 9547\nweighted avg 0.81 0.79 0.80 9547\n"}, "test": {"f1": 77.95008729588169, "recall": 77.49642638350011, "precision": 78.4090909090909, "summary": " precision recall f1-score support\n\n chemical 0.86 0.81 0.83 5377\n disease 0.70 0.73 0.72 4417\n\n micro avg 0.78 0.77 0.78 9794\n macro avg 0.78 0.77 0.77 9794\nweighted avg 0.79 0.77 0.78 9794\n"}}
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test_bc5cdr_span_lower.json
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{"valid": {"f1": 80.33508297545198, "recall": 79.35477113229287, "precision": 81.33991840240499, "summary": " precision recall f1-score support\n\n entity 0.81 0.79 0.80 9547\n\n micro avg 0.81 0.79 0.80 9547\n macro avg 0.81 0.79 0.80 9547\nweighted avg 0.81 0.79 0.80 9547\n"}, "test": {"f1": 78.75173512929926, "recall": 78.20093935062283, "precision": 79.3103448275862, "summary": " precision recall f1-score support\n\n entity 0.79 0.78 0.79 9794\n\n micro avg 0.79 0.78 0.79 9794\n macro avg 0.79 0.78 0.79 9794\nweighted avg 0.79 0.78 0.79 9794\n"}}
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test_bionlp2004_lower.json
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{"valid": {"f1": 71.65706973768394, "recall": 77.74178621008792, "precision": 66.45569620253164, "summary": " precision recall f1-score support\n\n cell line 0.47 0.62 0.53 500\n cell type 0.73 0.72 0.72 1920\n dna 0.65 0.74 0.69 1054\n protein 0.67 0.82 0.74 5052\n rna 0.61 0.80 0.69 118\n\n micro avg 0.66 0.78 0.72 8644\n macro avg 0.63 0.74 0.68 8644\nweighted avg 0.67 0.78 0.72 8644\n"}}
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test_bionlp2004_span_lower.json
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{"valid": {"f1": 76.9098712446352, "recall": 82.92457195742712, "precision": 71.70868347338936, "summary": " precision recall f1-score support\n\n entity 0.72 0.83 0.77 8644\n\n micro avg 0.72 0.83 0.77 8644\n macro avg 0.72 0.83 0.77 8644\nweighted avg 0.72 0.83 0.77 8644\n"}}
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test_conll2003_lower.json
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{"valid": {"f1": 85.48233871664219, "recall": 83.6120966379456, "precision": 87.4381625441696, "summary": " precision recall f1-score support\n\n location 0.90 0.81 0.85 1837\norganization 0.79 0.84 0.81 1341\n other 0.80 0.63 0.71 922\n person 0.95 0.96 0.95 1819\n\n micro avg 0.87 0.84 0.85 5919\n macro avg 0.86 0.81 0.83 5919\nweighted avg 0.87 0.84 0.85 5919\n"}, "test": {"f1": 82.81796181769293, "recall": 82.08955223880598, "precision": 83.5594139989148, "summary": " precision recall f1-score support\n\n location 0.87 0.79 0.83 1659\norganization 0.76 0.83 0.79 1660\n other 0.69 0.62 0.66 702\n person 0.95 0.94 0.94 1607\n\n micro avg 0.84 0.82 0.83 5628\n macro avg 0.82 0.79 0.80 5628\nweighted avg 0.84 0.82 0.83 5628\n"}}
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test_conll2003_span_lower.json
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{"valid": {"f1": 89.873417721519, "recall": 87.56546713971954, "precision": 92.30632235084595, "summary": " precision recall f1-score support\n\n entity 0.92 0.88 0.90 5919\n\n micro avg 0.92 0.88 0.90 5919\n macro avg 0.92 0.88 0.90 5919\nweighted avg 0.92 0.88 0.90 5919\n"}, "test": {"f1": 89.44904573280519, "recall": 88.27292110874201, "precision": 90.65693430656935, "summary": " precision recall f1-score support\n\n entity 0.91 0.88 0.89 5628\n\n micro avg 0.91 0.88 0.89 5628\n macro avg 0.91 0.88 0.89 5628\nweighted avg 0.91 0.88 0.89 5628\n"}}
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test_fin_lower.json
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+
{"valid": {"f1": 72.34848484848484, "recall": 73.46153846153847, "precision": 71.26865671641791, "summary": " precision recall f1-score support\n\n location 0.41 0.37 0.39 35\norganization 0.36 0.41 0.38 51\n other 1.00 0.17 0.29 6\n person 0.89 0.93 0.91 168\n\n micro avg 0.71 0.73 0.72 260\n macro avg 0.66 0.47 0.49 260\nweighted avg 0.72 0.73 0.72 260\n"}}
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test_fin_span_lower.json
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+
{"valid": {"f1": 75.37878787878788, "recall": 76.53846153846153, "precision": 74.25373134328358, "summary": " precision recall f1-score support\n\n entity 0.74 0.77 0.75 260\n\n micro avg 0.74 0.77 0.75 260\n macro avg 0.74 0.77 0.75 260\nweighted avg 0.74 0.77 0.75 260\n"}}
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test_mit_movie_trivia_lower.json
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+
{"valid": {"f1": 67.81115879828327, "recall": 70.8582483292297, "precision": 65.01532999838632, "summary": " precision recall f1-score support\n\n actor 0.88 0.94 0.91 1274\n award 0.41 0.45 0.43 66\ncharacter name 0.54 0.52 0.53 283\n date 0.95 0.97 0.96 661\n director 0.77 0.89 0.82 425\n genre 0.69 0.75 0.72 789\n opinion 0.42 0.45 0.43 195\n origin 0.29 0.35 0.32 190\n plot 0.44 0.50 0.47 1577\n quote 0.43 0.51 0.47 47\n relationship 0.45 0.47 0.46 171\n soundtrack 0.00 0.00 0.00 8\n\n micro avg 0.65 0.71 0.68 5686\n macro avg 0.52 0.57 0.54 5686\n weighted avg 0.66 0.71 0.68 5686\n"}}
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test_mit_movie_trivia_span_lower.json
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+
{"valid": {"f1": 72.20463683805286, "recall": 74.21737601125572, "precision": 70.29818424121272, "summary": " precision recall f1-score support\n\n entity 0.70 0.74 0.72 5686\n\n micro avg 0.70 0.74 0.72 5686\n macro avg 0.70 0.74 0.72 5686\nweighted avg 0.70 0.74 0.72 5686\n"}}
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test_mit_restaurant_lower.json
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{"valid": {"f1": 76.79975335285957, "recall": 79.05426848619486, "precision": 74.67026378896882, "summary": " precision recall f1-score support\n\n amenity 0.64 0.69 0.66 533\n cuisine 0.80 0.81 0.81 532\n dish 0.72 0.79 0.75 288\n location 0.82 0.84 0.83 812\n money 0.77 0.84 0.81 171\n rating 0.71 0.83 0.76 201\n restaurant 0.81 0.80 0.81 402\n time 0.59 0.69 0.64 212\n\n micro avg 0.75 0.79 0.77 3151\n macro avg 0.73 0.79 0.76 3151\nweighted avg 0.75 0.79 0.77 3151\n"}}
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test_mit_restaurant_span_lower.json
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+
{"valid": {"f1": 82.38787310517269, "recall": 83.65598222786417, "precision": 81.1576354679803, "summary": " precision recall f1-score support\n\n entity 0.81 0.84 0.82 3151\n\n micro avg 0.81 0.84 0.82 3151\n macro avg 0.81 0.84 0.82 3151\nweighted avg 0.81 0.84 0.82 3151\n"}}
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test_ontonotes5_lower.json
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{"valid": {"f1": 79.76935455033079, "recall": 80.83892009421997, "precision": 78.72772189871185, "summary": " precision recall f1-score support\n\n cardinal number 0.81 0.80 0.81 937\n date 0.80 0.82 0.81 1507\n event 0.46 0.34 0.39 143\n facility 0.28 0.35 0.31 115\ngeopolitical area 0.89 0.86 0.88 2261\n group 0.85 0.84 0.85 847\n language 0.84 0.64 0.72 33\n law 0.10 0.15 0.12 40\n location 0.30 0.55 0.39 203\n money 0.82 0.91 0.86 274\n ordinal number 0.84 0.82 0.83 232\n organization 0.72 0.75 0.73 1728\n percent 0.89 0.89 0.89 177\n person 0.88 0.92 0.90 2014\n product 0.51 0.51 0.51 72\n quantity 0.74 0.75 0.74 99\n time 0.66 0.71 0.69 214\n work of art 0.31 0.31 0.31 142\n\n micro avg 0.79 0.81 0.80 11038\n macro avg 0.65 0.66 0.65 11038\n weighted avg 0.80 0.81 0.80 11038\n"}, "test": {"f1": 81.69770615946815, "recall": 82.46378099724468, "precision": 80.94573372884314, "summary": " precision recall f1-score support\n\n cardinal number 0.83 0.82 0.83 934\n date 0.80 0.84 0.82 1601\n event 0.38 0.41 0.40 63\n facility 0.42 0.46 0.44 135\ngeopolitical area 0.93 0.89 0.91 2240\n group 0.82 0.83 0.83 841\n language 0.69 0.41 0.51 22\n law 0.33 0.33 0.33 40\n location 0.32 0.50 0.39 179\n money 0.82 0.87 0.84 314\n ordinal number 0.80 0.87 0.83 195\n organization 0.75 0.77 0.76 1792\n percent 0.89 0.89 0.89 348\n person 0.91 0.93 0.92 1988\n product 0.53 0.41 0.46 76\n quantity 0.72 0.75 0.74 105\n time 0.55 0.60 0.58 212\n work of art 0.32 0.27 0.29 166\n\n micro avg 0.81 0.82 0.82 11251\n macro avg 0.66 0.66 0.65 11251\n weighted avg 0.81 0.82 0.82 11251\n"}}
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test_ontonotes5_span_lower.json
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{"valid": {"f1": 85.20369119963989, "recall": 85.74017032071028, "precision": 84.67388386865885, "summary": " precision recall f1-score support\n\n entity 0.85 0.86 0.85 11038\n\n micro avg 0.85 0.86 0.85 11038\n macro avg 0.85 0.86 0.85 11038\nweighted avg 0.85 0.86 0.85 11038\n"}, "test": {"f1": 86.06291875582376, "recall": 86.19678250822149, "precision": 85.92947013999645, "summary": " precision recall f1-score support\n\n entity 0.86 0.86 0.86 11251\n\n micro avg 0.86 0.86 0.86 11251\n macro avg 0.86 0.86 0.86 11251\nweighted avg 0.86 0.86 0.86 11251\n"}}
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test_panx_dataset-en_lower.json
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{"valid": {"f1": 76.06894850012337, "recall": 76.48142897646724, "precision": 75.6608933454877, "summary": " precision recall f1-score support\n\n location 0.77 0.78 0.78 4799\norganization 0.66 0.65 0.65 4677\n person 0.83 0.87 0.85 4632\n\n micro avg 0.76 0.76 0.76 14108\n macro avg 0.75 0.77 0.76 14108\nweighted avg 0.75 0.76 0.76 14108\n"}, "test": {"f1": 75.15983047194887, "recall": 75.36555499531802, "precision": 74.95522601905581, "summary": " precision recall f1-score support\n\n location 0.76 0.75 0.75 4626\norganization 0.66 0.64 0.65 4744\n person 0.83 0.88 0.85 4513\n\n micro avg 0.75 0.75 0.75 13883\n macro avg 0.75 0.76 0.75 13883\nweighted avg 0.75 0.75 0.75 13883\n"}}
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test_panx_dataset-en_span_lower.json
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{"valid": {"f1": 86.47838040489877, "recall": 85.8378225120499, "precision": 87.12857040074825, "summary": " precision recall f1-score support\n\n entity 0.87 0.86 0.86 14108\n\n micro avg 0.87 0.86 0.86 14108\n macro avg 0.87 0.86 0.86 14108\nweighted avg 0.87 0.86 0.86 14108\n"}, "test": {"f1": 86.1585521284731, "recall": 85.21212994309587, "precision": 87.12623361319783, "summary": " precision recall f1-score support\n\n entity 0.87 0.85 0.86 13883\n\n micro avg 0.87 0.85 0.86 13883\n macro avg 0.87 0.85 0.86 13883\nweighted avg 0.87 0.85 0.86 13883\n"}}
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test_wnut2017_lower.json
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+
{"valid": {"f1": 51.15273775216138, "recall": 42.464114832535884, "precision": 64.31159420289855, "summary": " precision recall f1-score support\n\n corporation 0.00 0.00 0.00 34\n group 0.00 0.00 0.00 39\n location 0.52 0.23 0.32 74\n person 0.74 0.65 0.69 470\n product 0.65 0.19 0.30 114\n work of art 0.18 0.09 0.12 105\n\n micro avg 0.64 0.42 0.51 836\n macro avg 0.35 0.19 0.24 836\nweighted avg 0.57 0.42 0.47 836\n"}, "test": {"f1": 43.725156161272004, "recall": 35.74744661095636, "precision": 56.28654970760234, "summary": " precision recall f1-score support\n\n corporation 0.00 0.00 0.00 66\n group 0.50 0.08 0.15 165\n location 0.55 0.34 0.42 150\n person 0.60 0.68 0.64 428\n product 0.27 0.06 0.10 127\n work of art 0.42 0.16 0.23 141\n\n micro avg 0.56 0.36 0.44 1077\n macro avg 0.39 0.22 0.26 1077\nweighted avg 0.48 0.36 0.38 1077\n"}}
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test_wnut2017_span_lower.json
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+
{"valid": {"f1": 57.84526391901663, "recall": 47.84688995215311, "precision": 73.12614259597807, "summary": " precision recall f1-score support\n\n entity 0.73 0.48 0.58 836\n\n micro avg 0.73 0.48 0.58 836\n macro avg 0.73 0.48 0.58 836\nweighted avg 0.73 0.48 0.58 836\n"}, "test": {"f1": 49.943117178612056, "recall": 40.76137418755803, "precision": 64.4640234948605, "summary": " precision recall f1-score support\n\n entity 0.64 0.41 0.50 1077\n\n micro avg 0.64 0.41 0.50 1077\n macro avg 0.64 0.41 0.50 1077\nweighted avg 0.64 0.41 0.50 1077\n"}}
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tokenizer_config.json
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{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 512, "name_or_path": "xlm-roberta-base"}
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