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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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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: deberta-v3-base-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: 0.7739696312364425 |
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- name: Recall |
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type: recall |
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value: 0.7962863774326013 |
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- name: F1 |
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type: f1 |
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value: 0.7849694196330357 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9769126125154315 |
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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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# deberta-v3-base-company-names |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the nbroad/company_names dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0693 |
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- Precision: 0.7740 |
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- Recall: 0.7963 |
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- F1: 0.7850 |
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- Accuracy: 0.9769 |
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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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| 0.0752 | 1.0 | 2126 | 0.0664 | 0.7416 | 0.7979 | 0.7687 | 0.9757 | |
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| 0.0484 | 2.0 | 4252 | 0.0652 | 0.7725 | 0.7903 | 0.7813 | 0.9768 | |
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| 0.0415 | 3.0 | 6378 | 0.0693 | 0.7740 | 0.7963 | 0.7850 | 0.9769 | |
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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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