Instructions to use kbnlresearch/indo2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbnlresearch/indo2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kbnlresearch/indo2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kbnlresearch/indo2") model = AutoModelForMaskedLM.from_pretrained("kbnlresearch/indo2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Indonesian_1800-2000
Browse files- added_tokens.json +1 -0
- config.json +31 -0
- eval_results.txt +15 -0
- model_config.json +1 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
added_tokens.json
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{}
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config.json
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{
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"finetuning_task": "ner",
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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-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"num_labels": 20,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"pruned_heads": {},
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"torchscript": false,
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"type_vocab_size": 2,
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"vocab_size": 119547
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}
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eval_results.txt
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precision recall f1-score support
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DAT 0.0000 0.0000 0.0000 0
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DOM 0.0000 0.0000 0.0000 0
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LOC 0.2186 0.1942 0.2057 278
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MISC 0.0000 0.0000 0.0000 423
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ORG 0.4648 0.2095 0.2888 315
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PER 0.7143 0.3933 0.5072 356
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QUA 0.0000 0.0000 0.0000 0
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REF 0.0000 0.0000 0.0000 0
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SEP] 0.0000 0.0000 0.0000 0
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micro avg 0.3190 0.1895 0.2378 1372
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macro avg 0.1553 0.0886 0.1113 1372
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weighted avg 0.3363 0.1895 0.2396 1372
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model_config.json
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{"bert_model": "bert-base-multilingual-cased", "do_lower": false, "max_seq_length": 180, "num_labels": 20, "label_map": {"1": "O", "2": "B-PER", "3": "I-PER", "4": "B-ORG", "5": "I-ORG", "6": "B-LOC", "7": "I-LOC", "8": "B-DAT", "9": "I-DAT", "10": "B-REF", "11": "I-REF", "12": "B-DOM", "13": "I-DOM", "14": "B-QUA", "15": "I-QUA", "16": "B-MISC", "17": "I-MISC", "18": "[CLS]", "19": "[SEP]"}}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:068405383bef65839e0671a8ac461f116a74e184a2a60a588a32a800845ac081
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size 711525557
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer_config.json
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{"do_lower_case": false, "max_len": 512, "init_inputs": []}
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vocab.txt
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