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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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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: scibert-finetuned-ner |
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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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# scibert-finetuned-ner |
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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: 0.3459 |
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- Precision: 0.5666 |
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- Recall: 0.5191 |
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- F1: 0.5418 |
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- Accuracy: 0.9363 |
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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: 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: 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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| No log | 1.0 | 121 | 0.3648 | 0.3157 | 0.3390 | 0.3269 | 0.8945 | |
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| No log | 2.0 | 242 | 0.3177 | 0.5280 | 0.3348 | 0.4097 | 0.9253 | |
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| No log | 3.0 | 363 | 0.2599 | 0.5143 | 0.4326 | 0.4700 | 0.9315 | |
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| No log | 4.0 | 484 | 0.2825 | 0.5360 | 0.4227 | 0.4726 | 0.9336 | |
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| 0.2574 | 5.0 | 605 | 0.2968 | 0.5473 | 0.4922 | 0.5183 | 0.9350 | |
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| 0.2574 | 6.0 | 726 | 0.3193 | 0.5857 | 0.4894 | 0.5332 | 0.9377 | |
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| 0.2574 | 7.0 | 847 | 0.3327 | 0.5513 | 0.4879 | 0.5177 | 0.9356 | |
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| 0.2574 | 8.0 | 968 | 0.3315 | 0.5658 | 0.5121 | 0.5376 | 0.9363 | |
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| 0.0678 | 9.0 | 1089 | 0.3413 | 0.5465 | 0.5163 | 0.5310 | 0.9361 | |
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| 0.0678 | 10.0 | 1210 | 0.3459 | 0.5666 | 0.5191 | 0.5418 | 0.9363 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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