Question Answering
Transformers
Safetensors
biology
medical
biomedical
natural-language-processing
relation-extraction
named-entity-recognition
synthetic-biology
synthetic-data
low-resource
biomedbert
Instructions to use szd0709/MultiAgent4SyntheticBioNLP-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use szd0709/MultiAgent4SyntheticBioNLP-models with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="szd0709/MultiAgent4SyntheticBioNLP-models")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("szd0709/MultiAgent4SyntheticBioNLP-models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full
Browse files- overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/config.json +66 -0
- overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/model.safetensors +3 -0
- overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/tokenizer.json +0 -0
- overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/tokenizer_config.json +15 -0
- overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/training_args.bin +3 -0
overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": null,
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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": "O",
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"1": "B-BEH",
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"2": "B-COND",
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"3": "B-CTRL",
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"4": "B-DRUG",
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"5": "B-OTHER",
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"6": "B-PHYS",
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"7": "B-RADIO",
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"8": "B-SURG",
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"9": "I-BEH",
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"10": "I-COND",
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"11": "I-CTRL",
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"12": "I-DRUG",
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"13": "I-OTHER",
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"14": "I-PHYS",
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"15": "I-RADIO",
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"16": "I-SURG"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"label2id": {
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"B-BEH": 1,
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"B-COND": 2,
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"B-CTRL": 3,
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"B-DRUG": 4,
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"B-OTHER": 5,
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"B-PHYS": 6,
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"B-RADIO": 7,
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"B-SURG": 8,
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"I-BEH": 9,
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"I-COND": 10,
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"I-CTRL": 11,
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"I-DRUG": 12,
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"I-OTHER": 13,
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"I-PHYS": 14,
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"I-RADIO": 15,
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"I-SURG": 16,
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"O": 0
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},
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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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"pad_token_id": 0,
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"tie_word_embeddings": true,
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"transformers_version": "5.16.1",
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"type_vocab_size": 2,
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"use_cache": false,
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"vocab_size": 30522
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}
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overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d62f3cc72dc1a5ec67855a5723a9f80157fb97d073cdc41e3dc142ffec004f7a
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size 435642228
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overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/tokenizer.json
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overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"is_local": false,
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"local_files_only": false,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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overall-results/neurotrial-ner/neurotrial_ner_biomedbert_raw_synthetic_full/training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a3ff28594b8ce7b5175bd21f0d33a47d171968288b34a9afa7a30d489cc077e3
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size 5329
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