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Push ../models/xlnet/xlnet-base-cased/biored-augmentations-only-super/ trained on biored-train_200_splits.pt (200 samples)

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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: xlnet-base-cased
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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/xlnet-base-cased-biored-augmented-super
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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/xlnet-base-cased-biored-augmented-super
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+
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+ This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2035
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+ - Accuracy: 0.9315
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+ - Precision: 0.8447
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+ - Recall: 0.8503
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+ - F1: 0.8469
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+ - Weighted F1: 0.9318
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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 | 0.2497 | 0.9156 | 0.8595 | 0.7951 | 0.8242 | 0.9144 |
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+ | No log | 2.0 | 50 | 0.2404 | 0.9215 | 0.843 | 0.838 | 0.8404 | 0.9213 |
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+ | No log | 3.0 | 75 | 0.2595 | 0.9142 | 0.82 | 0.8571 | 0.8369 | 0.9161 |
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+ | No log | 4.0 | 100 | 0.2448 | 0.9266 | 0.8539 | 0.8261 | 0.8396 | 0.9257 |
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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
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+ ],
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+ "untie_r": true,
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