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Push distilbert-base-uncased trained on biored-original_splits.pt

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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: mit
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+ base_model: distilbert-base-uncased
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+ tags:
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+ - low-resource NER
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+ - token_classification
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+ - biomedicine
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+ - medical NER
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+ - generated_from_trainer
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+ datasets:
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+ - medicine
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: Dagobert42/distilbert-base-uncased-biored-finetuned
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # Dagobert42/distilbert-base-uncased-biored-finetuned
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the bigbio/biored dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6976
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+ - Accuracy: 0.7703
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+ - Precision: 0.5335
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+ - Recall: 0.424
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+ - F1: 0.4652
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+ - Weighted F1: 0.7512
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
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+ | No log | 1.0 | 25 | 0.9181 | 0.7144 | 0.4183 | 0.1593 | 0.151 | 0.6108 |
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+ | No log | 2.0 | 50 | 0.8580 | 0.7283 | 0.5273 | 0.2252 | 0.2508 | 0.6404 |
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+ | No log | 3.0 | 75 | 0.8232 | 0.7369 | 0.5603 | 0.2769 | 0.3173 | 0.6638 |
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+ | No log | 4.0 | 100 | 0.7814 | 0.7476 | 0.5184 | 0.3618 | 0.4085 | 0.7031 |
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+ | No log | 5.0 | 125 | 0.7691 | 0.7507 | 0.5306 | 0.3929 | 0.4283 | 0.7173 |
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+ | No log | 6.0 | 150 | 0.7492 | 0.7607 | 0.5494 | 0.3919 | 0.4396 | 0.7244 |
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+ | No log | 7.0 | 175 | 0.7616 | 0.7622 | 0.5553 | 0.4048 | 0.4481 | 0.728 |
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+ | No log | 8.0 | 200 | 0.7256 | 0.7657 | 0.5437 | 0.4306 | 0.4717 | 0.7426 |
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+ | No log | 9.0 | 225 | 0.7413 | 0.7684 | 0.5565 | 0.4315 | 0.4739 | 0.7422 |
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+ | No log | 10.0 | 250 | 0.7497 | 0.7721 | 0.5606 | 0.4364 | 0.4789 | 0.7446 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.12.0
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+ - Tokenizers 0.15.0
config.json ADDED
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+ {
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+ "_name_or_path": "distilbert-base-uncased",
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+ "id2label": {
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+ "0": "null",
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+ "1": "GeneOrGeneProduct",
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+ "2": "DiseaseOrPhenotypicFeature",
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+ "3": "ChemicalEntity",
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+ "initializer_range": 0.02,
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+ "CellLine": 6,
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+ "ChemicalEntity": 3,
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+ "GeneOrGeneProduct": 1,
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+ "OrganismTaxon": 4,
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+ "SequenceVariant": 5,
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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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+ "tie_weights_": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.2",
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+ }
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