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Training complete
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README.md
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---
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base_model: jsylee/scibert_scivocab_uncased-finetuned-ner
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: AI_Workshop_Sonatafy_scibert-finetuned_ADEs
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results: []
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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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# AI_Workshop_Sonatafy_scibert-finetuned_ADEs
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This model is a fine-tuned version of [jsylee/scibert_scivocab_uncased-finetuned-ner](https://huggingface.co/jsylee/scibert_scivocab_uncased-finetuned-ner) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2103
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- Precision: 0.6454
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- Recall: 0.6510
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- F1: 0.6482
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- Accuracy: 0.9098
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-07
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.2952 | 1.0 | 640 | 0.2368 | 0.5806 | 0.6360 | 0.6071 | 0.8992 |
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| 0.2315 | 2.0 | 1280 | 0.2196 | 0.6321 | 0.6403 | 0.6362 | 0.9047 |
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| 0.2197 | 3.0 | 1920 | 0.2143 | 0.6340 | 0.6510 | 0.6424 | 0.9061 |
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| 0.2105 | 4.0 | 2560 | 0.2112 | 0.6467 | 0.6488 | 0.6478 | 0.9092 |
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| 0.2078 | 5.0 | 3200 | 0.2103 | 0.6454 | 0.6510 | 0.6482 | 0.9098 |
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### Framework versions
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- Transformers 4.41.1
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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