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README.md
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---
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base_model: allenai/scibert_scivocab_cased
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tags:
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- generated_from_trainer
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model-index:
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- name: SOMD-scibert-stage2-v1
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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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# SOMD-scibert-stage2-v1
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This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0115
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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: 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: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 1.0 | 202 | 0.4607 |
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| No log | 1.99 | 404 | 0.2408 |
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| 0.5891 | 2.99 | 606 | 0.1086 |
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| 0.5891 | 3.98 | 808 | 0.0879 |
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| 0.1352 | 4.98 | 1010 | 0.0505 |
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| 0.1352 | 5.97 | 1212 | 0.0286 |
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| 0.1352 | 6.97 | 1414 | 0.0262 |
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| 0.0541 | 7.96 | 1616 | 0.0231 |
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| 0.0541 | 8.96 | 1818 | 0.0224 |
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| 0.0395 | 9.95 | 2020 | 0.0217 |
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| 0.0395 | 10.95 | 2222 | 0.0191 |
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| 0.0395 | 11.94 | 2424 | 0.0156 |
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| 0.0303 | 12.94 | 2626 | 0.0164 |
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| 0.0303 | 13.93 | 2828 | 0.0146 |
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| 0.0226 | 14.93 | 3030 | 0.0124 |
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| 0.0226 | 15.92 | 3232 | 0.0127 |
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| 0.0226 | 16.92 | 3434 | 0.0115 |
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| 0.0176 | 17.91 | 3636 | 0.0123 |
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| 0.0176 | 18.91 | 3838 | 0.0115 |
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| 0.0146 | 19.9 | 4040 | 0.0115 |
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### Framework versions
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- Transformers 4.38.1
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- Pytorch 2.1.0+cu121
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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model.safetensors
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runs/Feb28_04-56-00_492d0b40593e/events.out.tfevents.1709096161.492d0b40593e.213.1
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size 12513
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