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--- |
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license: mit |
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base_model: microsoft/xtremedistil-l6-h256-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- nbroad/company_names |
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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: xtremedistil-l6-h256-company-names |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: nbroad/company_names |
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type: nbroad/company_names |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6998602375960866 |
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- name: Recall |
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type: recall |
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value: 0.7154210197339048 |
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- name: F1 |
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type: f1 |
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value: 0.7075550845586612 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9702296390871982 |
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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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# xtremedistil-l6-h256-company-names |
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This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the nbroad/company_names dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0789 |
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- Precision: 0.6999 |
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- Recall: 0.7154 |
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- F1: 0.7076 |
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- Accuracy: 0.9702 |
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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: 8e-05 |
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- train_batch_size: 48 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3.0 |
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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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| 0.1052 | 1.0 | 2126 | 0.0854 | 0.6824 | 0.6605 | 0.6713 | 0.9678 | |
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| 0.0724 | 2.0 | 4252 | 0.0814 | 0.6925 | 0.7042 | 0.6983 | 0.9696 | |
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| 0.0778 | 3.0 | 6378 | 0.0789 | 0.6999 | 0.7154 | 0.7076 | 0.9702 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.14.1 |
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