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--- |
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license: mit |
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base_model: m3rg-iitd/matscibert |
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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: MatSciBERT_BIOMAT_NER2 |
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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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# MatSciBERT_BIOMAT_NER2 |
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This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5936 |
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- Precision: 0.9555 |
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- Recall: 0.9490 |
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- F1: 0.9523 |
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- Accuracy: 0.9426 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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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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| 0.0537 | 1.0 | 1171 | 0.3242 | 0.9568 | 0.9456 | 0.9512 | 0.9406 | |
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| 0.0163 | 2.0 | 2342 | 0.3726 | 0.9578 | 0.9499 | 0.9538 | 0.9447 | |
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| 0.0054 | 3.0 | 3513 | 0.4411 | 0.9565 | 0.9496 | 0.9530 | 0.9432 | |
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| 0.0029 | 4.0 | 4684 | 0.4982 | 0.9562 | 0.9480 | 0.9521 | 0.9420 | |
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| 0.0017 | 5.0 | 5855 | 0.5276 | 0.9548 | 0.9472 | 0.9510 | 0.9404 | |
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| 0.001 | 6.0 | 7026 | 0.5463 | 0.9562 | 0.9494 | 0.9528 | 0.9429 | |
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| 0.0005 | 7.0 | 8197 | 0.5741 | 0.9567 | 0.9500 | 0.9533 | 0.9437 | |
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| 0.0004 | 8.0 | 9368 | 0.5979 | 0.9557 | 0.9494 | 0.9526 | 0.9428 | |
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| 0.0003 | 9.0 | 10539 | 0.6028 | 0.9558 | 0.9496 | 0.9527 | 0.9428 | |
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| 0.0004 | 10.0 | 11710 | 0.5936 | 0.9555 | 0.9490 | 0.9523 | 0.9426 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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