first model version
Browse files- .gitignore +2 -0
- README.md +18 -4
- config.json +149 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.json +0 -0
.gitignore
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.DS_Store
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.idea/
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README.md
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# CoReNer
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A multi-task model for named-entity recognition, relation extraction, entity mention detection and coreference resolution.
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See details and usage examples at: https://github.com/aiola-lab/corener
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---
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license: apache-2.0
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---
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language:
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- en
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tags:
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- NER
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- named entity recognition
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- RE
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- relation extraction
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- entity mention detection
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- EMD
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- coreference resolution
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license: apache-2.0
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datasets:
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- Ontonotes
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- CoNLL04
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---
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# CoReNer
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A multi-task model for named-entity recognition, relation extraction, entity mention detection and coreference resolution.
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See details and usage examples at: https://github.com/aiola-lab/corener.
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config.json
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{
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"_name_or_path": "roberta-base",
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"architectures": [
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"RobertaForMaskedLM"
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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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"classifier_dropout": null,
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"corener_config": {
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"cls_token": 0,
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"max_pairs": 1000,
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"ner_classes": 19,
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"pad_token": 1,
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"relation_classes": 5,
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"size_embedding": 25
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},
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"eos_token_id": 2,
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.19.1",
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"type_vocab_size": 1,
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"types": {
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"entities": {
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"CARDINAL": {
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"short": "CARDINAL",
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"verbose": "CARDINAL"
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},
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"DATE": {
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"short": "DATE",
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"verbose": "DATE"
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},
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"EVENT": {
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"short": "EVENT",
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"verbose": "EVENT"
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},
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"FAC": {
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"short": "FAC",
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"verbose": "Buildings, airports, highways, bridges"
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},
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"GPE": {
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"short": "GPE",
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"verbose": "Countries, cities, states."
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},
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"LANGUAGE": {
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"short": "LANGUAGE",
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"verbose": "LANGUAGE"
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},
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"LAW": {
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"short": "LAW",
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"verbose": "LAW"
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},
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"LOC": {
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"short": "LOC",
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"verbose": "LOCATION"
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},
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"MONEY": {
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"short": "MONEY",
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"verbose": "MONEY"
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},
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"NORP": {
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"short": "NORP",
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"verbose": "Nationalities or religious or political groups"
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},
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"ORDINAL": {
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"short": "ORDINAL",
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"verbose": "ORDINAL"
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},
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"ORG": {
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"short": "ORG",
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"verbose": "ORGANIZATION"
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},
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"PERCENT": {
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"short": "PERCENT",
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"verbose": "PERCENT"
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},
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"PERSON": {
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"short": "PER",
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"verbose": "PERSON"
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},
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"PRODUCT": {
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"short": "PROD",
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"verbose": "PRODUCT"
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},
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"QUANTITY": {
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"short": "QUANTITY",
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"verbose": "QUANTITY"
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},
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"TIME": {
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"short": "TIME",
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"verbose": "TIME"
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},
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"WORK_OF_ART": {
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"short": "WORK_OF_ART",
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"verbose": "WORK_OF_ART"
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}
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},
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"mentions": {
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"MENTION": {
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"short": "MENTION",
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"verbose": "MENTION"
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}
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},
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"references": {
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"COREF": {
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"short": "COREF",
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"verbose": "COREF"
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}
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},
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"relations": {
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"Kill": {
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"short": "Kill",
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"symmetric": false,
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"verbose": "Kill"
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},
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"Live_In": {
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"short": "Live",
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"symmetric": false,
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"verbose": "Live in"
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},
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"Located_In": {
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"short": "LocIn",
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"symmetric": false,
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"verbose": "Located in"
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},
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"OrgBased_In": {
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"short": "OrgBI",
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"symmetric": false,
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"verbose": "Organization based in"
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},
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"Work_For": {
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"short": "Work",
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"symmetric": false,
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"verbose": "Work for"
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}
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}
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},
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"use_cache": true,
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"vocab_size": 50265
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}
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merges.txt
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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:a3bab81b34f26b5dedf4a0a9ecd5090883ecf1e6fcd61fb88f48264f406e5196
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size 525207305
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}
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tokenizer.json
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tokenizer_config.json
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{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "do_lower_case": false, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"}
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vocab.json
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