bikashpatra
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Browse files- README.md +26 -0
- checkpoint-64/config.json +104 -0
- checkpoint-64/model.safetensors +3 -0
- checkpoint-64/optimizer.pt +3 -0
- checkpoint-64/rng_state.pth +3 -0
- checkpoint-64/scheduler.pt +3 -0
- checkpoint-64/trainer_state.json +201 -0
- checkpoint-64/training_args.bin +3 -0
- config.json +104 -0
- model.safetensors +3 -0
- runs/Jul25_06-39-01_r-bikashpatra-argilla-ds-train-mpij26va-36565-x6bgo/events.out.tfevents.1721889541.r-bikashpatra-argilla-ds-train-mpij26va-36565-x6bgo.66.0 +2 -2
- runs/Jul25_06-39-01_r-bikashpatra-argilla-ds-train-mpij26va-36565-x6bgo/events.out.tfevents.1721889567.r-bikashpatra-argilla-ds-train-mpij26va-36565-x6bgo.66.1 +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- training_args.bin +3 -0
- training_params.json +30 -0
- vocab.txt +0 -0
README.md
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---
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tags:
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- autotrain
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- token-classification
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base_model: elastic/distilbert-base-uncased-finetuned-conll03-english
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widget:
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- text: "I love AutoTrain"
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datasets:
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- bikashpatra/claims_token_classification
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---
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# Model Trained Using AutoTrain
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- Problem type: Token Classification
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## Validation Metrics
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loss: 5.248876094818115
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precision: 0.0
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recall: 0.0
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f1: 0.0
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accuracy: 0.0026870229007633587
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checkpoint-64/config.json
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{
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"_name_or_path": "elastic/distilbert-base-uncased-finetuned-conll03-english",
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"_num_labels": 37,
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"activation": "gelu",
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"architectures": [
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"DistilBertForTokenClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"finetuning_task": "ner",
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"hidden_dim": 3072,
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"id2label": {
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"0": "B-Apparatus",
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"1": "B-Chemical Compound",
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"2": "I-Algorithm",
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"3": "B-Product",
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"4": "B-Composition of Matter",
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"5": "B-Machine",
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"6": "B-Algorithm",
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"7": "B-Biotechnology",
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"8": "B-Process",
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"9": "I-Electrical Circuit",
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"10": "I-Manufacture",
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"11": "B-Electrical Circuit",
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"12": "O",
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"13": "I-Software",
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"14": "I-Composition of Matter",
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"15": "I-Pharmaceutical Formulation",
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"16": "I-System",
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"17": "I-Hardware",
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"18": "I-Biotechnology",
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"19": "B-Device",
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"20": "B-Pharmaceutical Formulation",
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"21": "I-Product",
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"22": "I-Machine",
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"23": "B-Hardware",
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"24": "B-System",
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"25": "B-Software",
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"26": "I-Device",
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"27": "B-Design",
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"28": "I-Design",
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"29": "I-Process",
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"30": "I-Chemical Compound",
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"31": "B-Method of Use",
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"32": "I-Apparatus",
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"33": "I-Method of Use",
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"34": "B-Method",
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"35": "B-Manufacture",
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"36": "I-Method"
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},
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"initializer_range": 0.02,
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"label2id": {
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"B-Device": 19,
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"B-Electrical Circuit": 11,
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"B-Machine": 5,
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"B-Manufacture": 35,
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"B-Method": 34,
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"B-Method of Use": 31,
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"B-Pharmaceutical Formulation": 20,
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"B-Process": 8,
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"B-Product": 3,
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"B-Software": 25,
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"B-System": 24,
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"I-Algorithm": 2,
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"I-Apparatus": 32,
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"I-Biotechnology": 18,
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"O": 12
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.43.1",
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"vocab_size": 30522
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}
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checkpoint-64/model.safetensors
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checkpoint-64/optimizer.pt
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