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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- surrey-nlp/PLOD-unfiltered |
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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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widget: |
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- text: Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons. |
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- text: RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory |
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cortex in Figure 1. |
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- text: Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar |
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imaging (EPI). |
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base_model: albert-large-v2 |
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model-index: |
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- name: albert-large-v2-finetuned-ner_with_callbacks |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: surrey-nlp/PLOD-unfiltered |
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type: token-classification |
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args: PLODunfiltered |
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metrics: |
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- type: precision |
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value: 0.9655166719570215 |
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name: Precision |
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- type: recall |
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value: 0.9608483288141474 |
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name: Recall |
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- type: f1 |
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value: 0.9631768437660728 |
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name: F1 |
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- type: accuracy |
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value: 0.9589410429715819 |
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name: Accuracy |
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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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# albert-large-v2-finetuned-ner_with_callbacks |
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This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the [PLOD-unfiltered](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1235 |
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- Precision: 0.9655 |
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- Recall: 0.9608 |
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- F1: 0.9632 |
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- Accuracy: 0.9589 |
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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: 4 |
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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: 6 |
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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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| 0.1377 | 0.49 | 7000 | 0.1294 | 0.9563 | 0.9422 | 0.9492 | 0.9436 | |
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| 0.1244 | 0.98 | 14000 | 0.1165 | 0.9589 | 0.9504 | 0.9546 | 0.9499 | |
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| 0.107 | 1.48 | 21000 | 0.1140 | 0.9603 | 0.9509 | 0.9556 | 0.9511 | |
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| 0.1088 | 1.97 | 28000 | 0.1086 | 0.9613 | 0.9551 | 0.9582 | 0.9536 | |
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| 0.0918 | 2.46 | 35000 | 0.1059 | 0.9617 | 0.9582 | 0.9600 | 0.9556 | |
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| 0.0847 | 2.95 | 42000 | 0.1067 | 0.9620 | 0.9586 | 0.9603 | 0.9559 | |
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| 0.0734 | 3.44 | 49000 | 0.1188 | 0.9646 | 0.9588 | 0.9617 | 0.9574 | |
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| 0.0725 | 3.93 | 56000 | 0.1065 | 0.9660 | 0.9599 | 0.9630 | 0.9588 | |
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| 0.0547 | 4.43 | 63000 | 0.1273 | 0.9662 | 0.9602 | 0.9632 | 0.9590 | |
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| 0.0542 | 4.92 | 70000 | 0.1235 | 0.9655 | 0.9608 | 0.9632 | 0.9589 | |
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| 0.0374 | 5.41 | 77000 | 0.1401 | 0.9647 | 0.9613 | 0.9630 | 0.9586 | |
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| 0.0417 | 5.9 | 84000 | 0.1380 | 0.9641 | 0.9622 | 0.9632 | 0.9588 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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